Offner田野成像光谱仪光学系统建模与优化设计
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摘要
成像光谱仪是一种可同时采集目标光谱特征和空间特征的光学设备,可以实现图、谱合一,从而解决传统科学领域“成像无光谱”和“光谱不成像”的问题。相比于基于平面或凹面光栅的成像光谱仪,采用凸面光栅做分光元件的Offner成像光谱仪具有数值孔径大、无谱线弯曲、色畸变小、结构紧凑和加工装配相对简单等诸多优点。目前,Offner成像光谱仪已经广泛应用于卫星遥感、地质探测、精准农业、生产制造和军事侦察等领域。现有设计方法主要为数值光线追迹法和基于单光栅像差理论的解析设计方法。深入研究Offner成像光谱仪的成像特性及设计方法,具有重要的理论价值与现实意义。
本文的研究重点是Offner成像光谱仪光学系统的建模与优化设计。建模的目标是建立起像点与物点、系统参数之间的解析函数关系;优化的目标是,求取合适的系统结构参数和元件参数,使得系统的性能满足应用要求。主要研究内容包括以下几个方面:
(1)针对Offner成像光谱仪光学系统,基于费马原理,利用光线追迹法得到光学系统的数值模型,然后运用级数展开方法,推导出系统的3阶解析模型,亦即3阶像差理论。解析模型提供了像差系数关于结构参数、光栅参数和元件参数的解析函数关系。通过数值实验验证了解析模型的精度并分析了模型误差的来源。利用解析的像差系数分析了系统中彗差、球差和像散等主要像差的几何特征,丰富多元件光学系统的像差理论,加深对系统成像特性的理解,为Offner系统优化设计提供理论指导。
(2)结合理想成像条件,利用像差理论建立起光学系统优化设计问题的单目标优化模型。引入具有全局搜索功能的遗传算法进行优化,设计结果表明,遗传算法能够较好的解决Offner光学系统的优化设计问题。从光谱分辨率、谱线弯曲、色畸变和调制传递函数截止频率等方面验证了设计结果的性能。
(3)针对目标函数的计算复杂性,将复杂模型近似技术Kriging模型引入Offner光学系统的优化设计问题,提高优化设计效率。讨论了采样方法对Kriging模型精度的影响,及Kriging模型在非线性建模方面的性能。通过在设计空间中采集训练样本,建立起多个波长处像差系数和目标函数的Kriging模型。实验结果表明,在优化过程中,有机结合Kriging模型和精确解析模型,同时考虑多个波长处的系统性能,既可以提高优化设计的时间效率,又可以保证设计结果在全光谱范围内的最优性。
(4)针对优化问题的多目标特点,研究适合工程应用的多目标优化算法及其在Offner光学系统优化设计中的应用。为了提高多目标优化算法的效率和优化结果的实用性,提出了结合决策者偏好的多加权和优化算法与多加权和鲁棒优化算法,可以有效地缩小搜索空间,使优化结果分布在决策者感兴趣的区域,提高了解的分辨率。数值试验和设计实例验证了算法的性能。
这些研究工作不仅对Offner光学系统设计具有重要意义,也对将进化算法用于求解复杂工程优化问题具有重要的意义。
Imaging spectrometers are the optical instruments, which can simultaneously acquire the spectral and spatial characteristics of an object, and combine them into one image cube. It solves the problem of "no spectral imaging" and "non-spectral imaging" in the traditional fields of optical science.
Compared to the flat or concave grating-based imaging spectrometer, the Offner imaging spectrometer which utilizes a convex grating as the diffractive element takes the advantages of large numerical aperture, no Smile, small spectral Keystone, compact volume, relatively easy processing and assembly, and so on. Currently, Offner imaging spectrometers have been widely applied to the areas such as satellite remote sensing, geological exploration, precision agriculture, manufacturing, and military reconnaissance. The main design methods include the numerical ray-tracing and the analytical design based on the aberration theory of the signal grating. In both theoretical and practical aspects, it is a significant topic to study the imaging properties and optimal design of the Offner imaging spectrometer.
This thesis mainly focuses on modeling and optimization of the optical system in Offner imaging spectrometer. The goal of modeling is to build the analytical function among image point, object point and system parameters; and the goal of optimization is to provide the appropriate system parameters to meet application requirements. The main research works in this thesis are as follows:
(1) For the Offner optical system, the numerical model is derived using numerical ray-tracing method according to Fermat’s principle, and then a third order analytical model is obtained using series expansion method, which is also a third order aberration theory of the optical system. Analytical model provides aberration coefficients in analytical form, which are the analytical functions among structural parameters, grating parameters and element parameters. The accuracy of the analytical model is verified and the sources of model error are analyzed through numerical experiments. With the analytical aberration coefficients Coma, spherical aberration, and astigmatism and distortion are discussed in detail. This fills the gap in the aberration theory of multi-element optical system, makes a deeper understanding of the imaging properties of the system, and provides theoretical guidance for system optimization design.
(2) Considering the ideal imaging condition, a single optimization model is constructed using the third order aberration theory. Genetic algorithm is applied to determine the system parameters. The results show that genetic algorithm is capable of solving the optimal design problem with high performance. The optimal design results are validated from the aspects of spectral resolution, Smile, spectral Keystone, the cut-off frequency of modulation transfer function, and so on.
(3) Taking into account the computational complexity of the objective function, application of Kriging model which is an approximate technique for complex model speeds up the optimal design process of Offner optimal system. First, discusses the effect of different sampling methods on the accuracy of Kriging model, and then discusses the performance of Kriging model in non-linear modeling area. The Kriging models of aberration coefficients and merit functions at multiple design wavelengths are established through training data sampled from design space. Experimental results show that in the optimization process, the combination of Kriging model and the exact analytical model, taking into account the system performance at multiple wavelengths, can enhance the time efficiency of optimal design, but also ensure the optimality of design results in the whole spectral range.
(4) Considering the multi-objective properties of the design problem, multi-objective optimization algorithms suitable for engineering application are developed and applied to the Offner optical system design problem. To enhance the efficiency of the algorithms and the practicality of optimal results, decision maker’preference based on multiple weighted sums is incorporated into multi-objective evolutionary algorithms and robust algorithms. These two novel methods can effectively reduce the search space, make the optimal results located only in the region of decision-maker’s interest, so that improve the resolution of the resulting solutions. Both numerical experiments and design examples demonstrate the algorithm performance.
These studies are conducted for optimal design of Offner optical system, but they are also of great significance when applying evolutionary algorithm to solve other complex engineering optimization problems.
本文的研究重点是Offner成像光谱仪光学系统的建模与优化设计。建模的目标是建立起像点与物点、系统参数之间的解析函数关系;优化的目标是,求取合适的系统结构参数和元件参数,使得系统的性能满足应用要求。主要研究内容包括以下几个方面:
(1)针对Offner成像光谱仪光学系统,基于费马原理,利用光线追迹法得到光学系统的数值模型,然后运用级数展开方法,推导出系统的3阶解析模型,亦即3阶像差理论。解析模型提供了像差系数关于结构参数、光栅参数和元件参数的解析函数关系。通过数值实验验证了解析模型的精度并分析了模型误差的来源。利用解析的像差系数分析了系统中彗差、球差和像散等主要像差的几何特征,丰富多元件光学系统的像差理论,加深对系统成像特性的理解,为Offner系统优化设计提供理论指导。
(2)结合理想成像条件,利用像差理论建立起光学系统优化设计问题的单目标优化模型。引入具有全局搜索功能的遗传算法进行优化,设计结果表明,遗传算法能够较好的解决Offner光学系统的优化设计问题。从光谱分辨率、谱线弯曲、色畸变和调制传递函数截止频率等方面验证了设计结果的性能。
(3)针对目标函数的计算复杂性,将复杂模型近似技术Kriging模型引入Offner光学系统的优化设计问题,提高优化设计效率。讨论了采样方法对Kriging模型精度的影响,及Kriging模型在非线性建模方面的性能。通过在设计空间中采集训练样本,建立起多个波长处像差系数和目标函数的Kriging模型。实验结果表明,在优化过程中,有机结合Kriging模型和精确解析模型,同时考虑多个波长处的系统性能,既可以提高优化设计的时间效率,又可以保证设计结果在全光谱范围内的最优性。
(4)针对优化问题的多目标特点,研究适合工程应用的多目标优化算法及其在Offner光学系统优化设计中的应用。为了提高多目标优化算法的效率和优化结果的实用性,提出了结合决策者偏好的多加权和优化算法与多加权和鲁棒优化算法,可以有效地缩小搜索空间,使优化结果分布在决策者感兴趣的区域,提高了解的分辨率。数值试验和设计实例验证了算法的性能。
这些研究工作不仅对Offner光学系统设计具有重要意义,也对将进化算法用于求解复杂工程优化问题具有重要的意义。
Imaging spectrometers are the optical instruments, which can simultaneously acquire the spectral and spatial characteristics of an object, and combine them into one image cube. It solves the problem of "no spectral imaging" and "non-spectral imaging" in the traditional fields of optical science.
Compared to the flat or concave grating-based imaging spectrometer, the Offner imaging spectrometer which utilizes a convex grating as the diffractive element takes the advantages of large numerical aperture, no Smile, small spectral Keystone, compact volume, relatively easy processing and assembly, and so on. Currently, Offner imaging spectrometers have been widely applied to the areas such as satellite remote sensing, geological exploration, precision agriculture, manufacturing, and military reconnaissance. The main design methods include the numerical ray-tracing and the analytical design based on the aberration theory of the signal grating. In both theoretical and practical aspects, it is a significant topic to study the imaging properties and optimal design of the Offner imaging spectrometer.
This thesis mainly focuses on modeling and optimization of the optical system in Offner imaging spectrometer. The goal of modeling is to build the analytical function among image point, object point and system parameters; and the goal of optimization is to provide the appropriate system parameters to meet application requirements. The main research works in this thesis are as follows:
(1) For the Offner optical system, the numerical model is derived using numerical ray-tracing method according to Fermat’s principle, and then a third order analytical model is obtained using series expansion method, which is also a third order aberration theory of the optical system. Analytical model provides aberration coefficients in analytical form, which are the analytical functions among structural parameters, grating parameters and element parameters. The accuracy of the analytical model is verified and the sources of model error are analyzed through numerical experiments. With the analytical aberration coefficients Coma, spherical aberration, and astigmatism and distortion are discussed in detail. This fills the gap in the aberration theory of multi-element optical system, makes a deeper understanding of the imaging properties of the system, and provides theoretical guidance for system optimization design.
(2) Considering the ideal imaging condition, a single optimization model is constructed using the third order aberration theory. Genetic algorithm is applied to determine the system parameters. The results show that genetic algorithm is capable of solving the optimal design problem with high performance. The optimal design results are validated from the aspects of spectral resolution, Smile, spectral Keystone, the cut-off frequency of modulation transfer function, and so on.
(3) Taking into account the computational complexity of the objective function, application of Kriging model which is an approximate technique for complex model speeds up the optimal design process of Offner optimal system. First, discusses the effect of different sampling methods on the accuracy of Kriging model, and then discusses the performance of Kriging model in non-linear modeling area. The Kriging models of aberration coefficients and merit functions at multiple design wavelengths are established through training data sampled from design space. Experimental results show that in the optimization process, the combination of Kriging model and the exact analytical model, taking into account the system performance at multiple wavelengths, can enhance the time efficiency of optimal design, but also ensure the optimality of design results in the whole spectral range.
(4) Considering the multi-objective properties of the design problem, multi-objective optimization algorithms suitable for engineering application are developed and applied to the Offner optical system design problem. To enhance the efficiency of the algorithms and the practicality of optimal results, decision maker’preference based on multiple weighted sums is incorporated into multi-objective evolutionary algorithms and robust algorithms. These two novel methods can effectively reduce the search space, make the optimal results located only in the region of decision-maker’s interest, so that improve the resolution of the resulting solutions. Both numerical experiments and design examples demonstrate the algorithm performance.
These studies are conducted for optimal design of Offner optical system, but they are also of great significance when applying evolutionary algorithm to solve other complex engineering optimization problems.
引文
[Abbass2001] Abbass H., Sarker R., Newton C., PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems, IEEE Congress on Evolutionary Computation 2001, 971-978, 2001
[Adra2007] Adra S. F., Griffin I., Fleming P. J., A comparative study of progressive preference articulation techniques for multi-objective optimization, Lecture Notes in Computer Science, 4403:908_921, 2007
[Aichholzer2002] Aichholzer O., Aurenhammer F., Brandstatter B., Ebner T., Krasser H., Magele C., Muhlmann M., Renhart W., Evolution Strategy and Hierarchical Clustering, IEEE Transactions on Magnetics, vol.38, no.2, 1041-1044, 2002
[Alba2005] Alba E., Dorronsoro B., The exploration exploitation tradeoff in dynamic cellular genetic algorithms, IEEE Transactions on Evolutionary Computation, vol.9, no.2, 126-142, 2005
[Arnold2008] Arnold G., Helbert J., Hiesinger H., Hirsch H., Jessberger E.K., Peter G., and Walter I., Mercury radiometer and thermal infrared spectrometer: a novel thermal imaging spectrometer for the exploration of Mercury, J. Appl. Rem. Sens. 2, p. 23528, 2008
[Barducci2004] Barducci A., De Cosmo V., Marcoionni P., Pippi I., ALISEO: a new stationary imaging interferometer, SPIE, 5546: 262-270, 2004
[Barrico2006a] Barrico C., Antunes C.H., Robustness Analysis in Evolutionary Multi-objective Optimization with a Case Study in Electrical Distribution Networks, II European Latin American Workshop on Engineering Systems (SELASI II), Porto, Portugal, 2006
[Barrico2006b] Barrico C., Antunes C.H., A New Approach to Robustness Analysis in Multi-objective Optimizat ion, Proceedings of the 7th International Conference on Multi-objective Programming and Goal Programming (MOPGP06), Tours, France, June 2006
[Barrico2006c] Barrico C., Antunes C.H., Robustness Analysis in Multi-objective Optimization Using a Degree of Robustness Concept, Proceedings of the 2006 IEEE Congress on Computation Intelligence (WCCI2006), Vancouver, Canada, July 2006, 6778-6783
[Benson1978] Benson H.P., Existence of efficient solutions for vector maximization problems,Journal of Optimization Theory and Applications, vol.26, no.4, 569-580, 1978
[Beutler1945] Beutler H.G., The theory of the concave grating, J. Opt. Soc. Am., 35, 311-350, 1945
[Beyer2007] Beyer H.G., Sendhoff B., Robust optimization-a comprehensive survey, Computer methods in applied mechanics and engineering, Vol.196 no.33-34, 3190-3218, 2007
[Branke1998] Branke J., Creating robust solutions by means of an evolutionary algorithm, Parallel Problem Solving from Nature, 119–128, 1998
[Branke2001] Branke J., Kau?ler T., Schmeck H., Guidance in evolutionary multi-objective optimization, Advances in Engineering Software, vol.32, no.6, 499–508, 2001
[Branke2005a] Branke J., Deb K., Integrating user preferences into evolutionary multi-objective optimization, Knowledge Incorporation in Evolutionary Computation, 461-477, 2005
[Branke2005b] Branke J., Schmidt C., Faster convergence by means of fitness estimation, Soft Computing, vol.9, no.1, 13-20, 2005
[Breckinridge1996] Breckinridge J.B., Evolution of imaging spectrometry: Past, present and future, SPIE, 2819: 2-6, 1996
[Brown2005] Brown R.H., Baines K.H., Bellucci G., Bibring J.P., Buratti B.J., Bussoletti E., Capaccioni F., Cerroni P., Clark R.N., Coradini A., Cruikshank D.P., Drossart P., Formisano V., Jaumann R., Langevin Y., Matson D.L., McCord T.B., Mennella V., Miller E., Nelson R.M., Nicholson P.D., Sicardy B., Sotin C., The Cassini Visual and Infrared Mapping Spectrometer Investigation. Space Sci. Rev. 115 (1–4), 111–168, 2005
[Chan2008] Chan T.M., Man K.F., Tang K.S., Kwong S., A jumping gene paradigm for evolutionary multi-objective optimization, IEEE Transactions on Evolutionary Computation, vol.12, no.2, 143-59, 2008
[Chanes1955] Chanes A., Cooper W.W., Ferguson R.O., Optimal estimation of executive compensation by linear programming, Management Science, vol.1, no.2, 138-151, 1955
[Chaudhuri2010] Chaudhuri S., Deb K., An interactive evolutionary multi-objective optimization and decision making procedure, Applied Soft Computing, vol.10, no.2, 496-511, 2010
[Chrisp1983a] Chrisp M.P., Aberrations of holographic toroidal grating systems, Applied Optics, 22: 1508-1518, 1983
[Chrisp1983b] Chrisp M.P., X-ray spectrograph design, Applied Optics, 22: 1519-1529, 1983
[Chrisp1999] Chrisp M.P., Convex diffraction grating imaging spectrometer, U.S. Patent, 5880834, 1999
[Chung2004] Chung H., Alonso J., Multi-objective Optimization Using Approximation Model-Based Genetic Algorithms, Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, Aug. 30–Sept. 1, AIAA-2004-432, 2004
[Coello2001] Coello Coello C.A., Pulido G.T., A micro-genetic algorithm for multi-objective optimization. In: Spector L., Goodman E.D., Wu A., Langdon W.B., Voigt H.M., Gen M., eds. Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2001. San Francisco: Morgan Kaufmann Publishers, 274?282, 2001
[Coello2003a] Coello Coello C.A, Becerra R.L., Evolutionary multi-objective optimization using a cultural algorithm, IEEE Swarm Intelligence Symposium, 6-13, 2003
[Coello2003b] Coello Coello C.A, Sierra M.R., A co-evolutionary multi-objective evolutionary algorithm, The 2003 Congress on Evolutionary Computation, 2003. CEC'03, 482-489, 2003
[Cone2000] Corne D.W., Knowles J.D., Oates M.J., The Pareto-envelope based selection algorithm for multi-objective optimization. In:Schoenauer M., Deb K., Rudolph G., Yao X., Lutton E., Merelo J.J., Schwefel H.P., eds. Parallel Problem Solving from Nature, PPSN VI. LNCS, Berlin: Springer-Verlag, 869?878, 2000
[Cone2001] Corne D.W., Jerram N.R., Knowles J.D., Oates M.J., PESA-II: Region-Based selection in evolutionary multi-objective optimization. In: Spector L., Goodman E.D., Wu A., Langdon W.B., Voigt H.M., Gen M., eds. Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2001, San Francisco: Morgan Kaufmann Publishers, 283?290, 2001
[Coradini2004] Coradini A., Filacchione G., Capaccioni F., Cerroni P., Adriani A., Brown R.H., Langevin Y., Gondet B., CASSINI/VIMS-V at Jupiter: radiometric calibration test and data results. Planet. Space Sci. 52, 661–670, 2004
[Cvetkovic1999] Cvetkovic D., Parmee I.C., Use of Preferences for GA_based Multi-objective Optimisation. In GECCO_99: Proceedings of the Genetic and Evolutionary Com-putation Conference, vol.2, 1504-1509, 1999
[Danielson1974] Danielson A., Lindblom P., Focusing conditions of the spherical concave grating, Optik, vol.41, no.4, 441-451, 1974
[Das1986] Das N.C., Murty M., Flat Field Spectrograph Using Convex Holographic Diffraction Grating and Concave Mirror, Pramana, 27(1-2), 171-192, 1986
[Davis1999] Davis C.O., Kappus M., Bowles J., Fisher J., Antoniades J., Carney M., Calibration, Characterization and first Results with the Ocean PHILLS Hyperspectral Imager, Proceedings of the SPIE, V. 3753, 160-168, 1999
[Davis2002] Davis C.O., et al., Ocean PHILLS hyperspectral imager: design, characterization, and calibration, Optics Express, 10(4): 210-221, 2002
[Davis2006] Davis C.O., Carder K.L., Gao B.C., Lee Z.P., Bissett W.P., The development of imaging spectrometry of the coastal ocean, IEEE International Conference on Geoscience and Remote Sensing Symposium 2007, 1982—1985, 2007
[Deb1999] Deb K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evolutionary Computation Journal, vol.7, no.3, 205–230, 1999
[Deb2001a] Deb K., Multi-Objective optimization using evolutionary algorithms, Chicester, UK: John Wiley & Sons, 2001
[Deb2001b] Deb K., Pratap A., Meyarivan T., Constrained test problems for multi-objective evolutionary optimization. Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, 284–298, 2001
[Deb2002a] Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. On Evolutionary Computation, vol.6, no.2, 182?197, 2002
[Deb2002b] Deb K., Thiele L., Laumanns M., Zitzler E., Scalable multi-objective optimization test problems, Proc. Congr. Evol. Comput. -CEC’02, vol. 1, Piscataway, New Jersey, 825–830, 2002.
[Deb2005a] Deb K., Mohan M., Mishra S., Evaluating theε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions, Evolutionary Computation, vol.13, no. 4, 501-525, 2005
[Deb2005b] Deb K., Saxena D.K., On finding Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems, Kangal report 2005011, KanGAL, Indian institute of Technology, India, 2005
[Deb2005c] Deb K., Thiele L., Laumanns M., Zitzler E., Scalable test problems for evolutionary multi-objective optimization, Evolutionary Multi-objective Optimization, 105-145, 2005
[Deb2005d] Deb K, Gupta H., Searching for Robust Pareto optimal Solutions in Mult i-Objective Optimization, Proceedings of the Third International Conference on Evolutionary Multi-criterion Optimization (EMO2005), Guanajuato, Mexico, Lecture Notes on Computer Science, 150-164, 2005
[Deb2006] Deb K., Sundar J., Uday N., Chaudhuri S., Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms, International Journal of Computational Intelligence Research (IJCIR), vol.2, no.6, 273-286, 2006
[Deb2007] Deb K., Kumar A., Interactive evolutionary multi-objective optimization and decision-making using reference direction method, In: Proceedings of the Genetic andEvolutionary Computation Conference (GECCO-2007), New York: The Association of Computing Machinery (ACM), 781-788, 2007
[Demarez1999] Demarez V., Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model, International Journal of Remote Sensing, vol.20, no.5, 879-894, 1999
[Duban1991] Duban M., Third-generation Rowland holographic mounting, Applied Optics, vol.30, no.28, 4019-4025, 1991
[Duban1999A] Duban M., Theory and computation of three Cosmic Origin Spectrograph aspheric gratings recorded with a multimode deformable mirror, Applied Optics, vol.38, no.7, 1096-1102, 1999
[Duban1999B] Duban M., Third-generation holographic Rowland mounting: fourth-order theory, Applied Optics, vol.38, no.16, 3443-3449, 1999
[Elvidge1990] Elvidge C.D., Chen Z., Groeneveld D.P., Detection of trace quantities of green vegetation in 1990 AVIRIS data, Remote Sensing of Environment, vol.44,no.2-3, 271-279, 1993
[Erickson2001] Erickson M., Mayer A., Horn J., The niched Pareto genetic algorithm 2 applied to the design of groundwater remediation system. In: Zitzler E., Deb K., Thiele L., Coello Coello C.A., Corne D., eds. Proc. of the 1st Int’l Conf. on Evolutionary Multi-Criterion Optimization, EMO 2001. Berlin: Springer-Verlag, 681?695, 2001
[Ehlmann2008] Ehlmann B.L., et al., Orbital identification of carbonate-bearing rocks on Mars, Science, 322, 1828– 1832, doi:10.1126/science.1164759, 2008
[Fang2004] Fang H., Rais-Rohani M., Horstemeyer M., Multiobjective Crashworthiness Optimization With Radial Basis Functions, Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, Aug. 30–Sept. 1, AIAA-2004-4487, 2004
[Farina2002] Farina M., A Neural Network Based Generalized Response Surface Multiobjective Evolutionary Algorithm, Proceedings of Congress on Evolutionary Computation, IEEE, New York, 956–961, 2002.
[Fonseca1993] Fonseca C.M., Fleming P.J., Genetic algorithm for multi-objective optimization, Formulation, discussion and generation. In: Forrest S, ed. Proc. of the 5th Int’l Conf. on Genetic Algorithms, San Mateo: Morgan Kauffman Publishers, 416?423, 1993
[Geoffrion1968] Geoffrion A.M., Proper efficiency and the theory of vector optimization, Journal of Mathematical Analysis and Application, vol.41, 491-502, 1968
[Goel2005] Goel T., Mack Y., Haftka R.T., Shyy W., Queipo N.V., Interaction between grid and design space refinement for bluff body-facilitated mixing, Proceedings of the 43rd AIAA aerospace sciences meeting and exhibit, Reno, NV, Paper no. 2005–0125, AIAA, 2005
[Goldberg1989] Goldberg D.E., Genetic algorithm for search, optimization, and machine learning, Boston: Addison-Wesley Longman Publishing Co., Inc., 1989
[Goto1993] Goto K, Kurosaki T., Canonical formation for the geometrical optics of concave gratings, J Opt Soc Am, A, 10, 452-465, 1993
[Green1998] Green R.O., Eastwood M.L., Sarture C.M., Chrien T.G., Aronsson M., Chippendale B.J., Faust J.A., Pavri B.E., Chovit C.J., Solis M., Olah M.R., Williams O.,“Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),”Remote Sens. Environ. 65, 227–248, 1998
[Green2007]Green R.O., Asner G., Ungar S., Knox R., NASA Mission to Measure Global Plant Physiology and Functional Types. Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA,2007
[Green2008] Green R.O., Pieters C.M., Mouroulis P., Sellar G., Eastwood M., Geier S., Shea J., et al., Calibration, shipment and initial spacecraft integration of the Moon Mineralogy Mapper (M3) imaging spectrometer for the Chandrayaan-1 mission, Lunar and Planetary Institute Science Conference Abstracts, 39,1803, 2008
[Grosso2008] Grosso A., Jamali A., Locatelli M., Finding maximin Latin hypercube designs by iterated local search heuristics, European Journal of Operational Research, vol.197, no.2, 541–547, 2008
[Gunawan2004] Gunawan S., Azarm S., Non-gradient based parameter sensitivity estimation for single objective robust design optimization, ASME Journal of Mechanical Design, vol.126, 395-402, 2004
[Gupta2005] Gupta H., Deb K., Handling constraints in robust multi-objective optimization, In IEEE Congress on Evolutionary Computation (CEC-2005), Edinburgh, U. K, vol.1, 25-32, 2005
[Haber1950] Haber H., Torus grating, J. Opt. Soc. Am., 40: 153-165, 1950
[Harada1999] Harada T., Takahashi K., Sakuma H., Osyczka A., Optimum design of agrazing-incidence flat-field spectrograph with a spherical varied-line-space grating, Applied Optics, vol.38, no.13, 2743-2748, 1999
[Haimes1971] Haimes Y.Y., Lasdon L.S., Wismer D.A., On a bicriterion formulation of the problems of integrated systems identification and system optimization, IEEE Transactions System, Man, Cybernetics, vol.1, no.3, 296-297, 1971
[Hajela1992] Hajela P., Lin C.Y., Genetic search strategies in multicriterion optimal design, Structural Optimization, Vol.4, 99-107, 1992
[Hanne1999] Hanne T., On the convergence of multi-objective evolutionary algorithms, European Journal of Operational Research, vol.117, no.3, 553-564, 1999
[Harada1980] Harada T., Kita T., Mechanically ruled aberration-corrected concave gratings, Applied Optics, vol.19, no.23, 3987-3993, 1980
[Hettrick1983] Hettrick M., Bowyer S., Variable line-space gratings: new designs for use in grazing incidence spectrometers, Applied Optics, vol.22, no.24, 3921-3924, 1983
[Hettrick1984] Hettrick M., Aberration of varied line-space grazing incidence gratings in converging light beams, Applied Optics, vol.23, no.18, 3221-3235, 1984
[Hirsch 2004] Hirsch H., Optical design of MERTIS in BepiColombo MERTIS-TIS, ESA-ref. MER-IFP_PP-001, 32-35, 2004
[Horn1994] Horn J., Nafpliotis N., Goldberg D.E., A niched Pareto genetic algorithm for multi-objective optimization, In: Fogarty TC, ed. Proc. Of the 1st IEEE Congress on Evolutionary Computation, Piscataway: IEEE, 82?87, 1994
[Huband2006] Huband S., Hingston P., Barone L., While L., A review of multi-objective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., vol. 10, no. 5, 477–506, 2006
[Igel2007] Igel C., Hansen N., Roth S., Covariance matrix adaptation for multi-objective optimization, Evolutionary Computation, Vol.15, no.1, 1-28, 2007
[Ishiguro1979] Ishiguro E., Iwanaga R., Oshio T., Geometrical optical theory of diffraction grating, Journal of Optical Society of America, vol.69, no.11, 1530-1538, 1979
[Ishibuchi2003] Ishibuchi H., Yoshida T., Murata T., Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling, IEEE Transactions on Evolutionary Computation, vol.7, no.2, 204-223, 2003
[Jaumann2006] Jaumann R., Stephan K., Brown R.H., McCord T.B., Coradini A., Capaccioni F., Filacchione G., Clark R.N., Cerroni P., Baines K.H., Bellucci G., Bibring J.P., Buratti B.J., Bussoletti E., Combes M., Cruikshank D.P., Drossart P., Formisano V., Langevin Y., Matson D.L., Nelson R.M., Nicholson P.D., Sicardy B., Sotin C., Roatsch T., Scholten F., Matz K.D., High resolution Cassini-VIMS mosaics of Titan and the icy saturnian satellites. Planet. Space Sci., 54, 1146-1155, 2006
[Jiao2005]Jiao L.C., Gong M.G., Shang R.H., Du H.F., Lu B., Clonal selection with Immune dominance and energy based multi-objective optimization. In: Coello Coello C.A., Aguirre A.H., Zitzler E., eds. Proc. of the 3rd Int’l Conf. on Evolutionary Multi-Criterion Optimization, EMO 2005. Berlin: Springer-Verlag, 2005. 474?489.
[Jin2003] Jin Y., Sendhoff B., Trade-off between performance and robustness: An evolutionary multi-objective approach. In EMO2003, 237–251, 2003
[Jin2005a] Jin Y., A Comprehensive Survey of Fitness Approximation in Evolutionary Computation, Soft Comput., Vol.9, no.1, 3–12, 2005
[Jin2005b] Jin Y., Branke J., Evolutionary optimization in uncertain environments-A survey, IEEE Transactions on Evolutionary Computation, vol.9, no.3, 303- 317, 2005
[Johnson2004] Johnson W.R., Wilson D.W., Bearman G., Backlund J., An all-reflective computed tomography imaging spectrometer, Proc. SPIE 5660, 88 , 2004
[Johnson2005] Johnson W.R., Wilson D.W., Bearman G., All-reflective snapshot hyperspectral imager for ultraviolet and infrared applications. Opt. Lett. 30, 1464–1466, 2005
[Johnson2007] Johnson W.R., Wilson D.W., Fink W., Humayun M., Bearman G., Snapshot hyperspectral imaging in ophthalmology, Journal of Biomedical Optics, 12, 014036, 2007
[Jones1998] Jones D.R., Schonlau M., Welch W.J., Efficient global optimization of expensive black-box functions, Journal of Global 0ptimization, vol.13, 445-492, 1998
[Joseph2007] Joseph S., Kang H., Chakraborty U., Optical Design with Epsilon-Dominated Multi-objective Evolutionary Algorithm, Adaptive and Natural Computing Algorithms, 77-84, 2007
[Knowles2000] Knowles J.D., Corne D.W., Approximating the non-dominated front using the Pareto archived evolution strategy, Evolutionary Computation, vol.8, no.2, 149?172, 2000
[Knowles2003] Knowles J., Corne D., Properties of an adaptive archiving algorithm for storing nondominated vectors, IEEE Transactions on Evolutionary Computation, vol.7, no.2, 100-116, 2003
[Kwo1987] Kwo D., Lawrence G.L., Chrisp M., Design of a grating spectrometer from a 1:1 Offner mirror system, SPIE, 818, 275-279, 1987
[Laumanns2002a] Laumanns M., Ocenasek J., Bayesian optimization algorithms for multi-objective Optimization. In: Merelo J.J., Adamidis P., Beyer H.G., eds. Proc. of the 7th Int’l Conf. on Parallel Problem Solving from Nature. London: Springer-Verlag, 298?307, 2002
[Laumanns2002b] Laumanns M., Thiele L., Deb K., Zitzler E., Combining convergence and diversity in evolutionary multi-objective optimization, Evolutionary Computation, vol.10, no.3, 263-282, 2002
[Li2003] Li X. A non-dominated sorting particle swarm optimizer for multi-objective optimization. In: Cantú-Paz E., Foster J.A., Deb K., Lawrence D., Roy R., eds. Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2003. Berlin: Springer-Verlag, 37?48, 2003
[Li2006] Li M., Azarm S., Boyars A., A New Deterministic Approach Using Sensitivity Region Measures for Multi-objective Robust and Feasibility Robust Design Optimization, ASME J. Mech. Des., vol.128, no.4, 874–883, 2006
[Li2008] Li M., Li G., Azarm S., A Kriging metamodel assisted multi-objective genetic algorithm for design optimization, Journal of Mechanical Design, Vol.130, 031401, 2008
[Li2009] Li H., Zhang Q., Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Transactions on Evolutionary Computation, vol.13, no.2, 184-302, 2009
[Lian2004] Lian Y., Liou M., Multiobjective Optimization Using Coupled Response Surface Model and Evolutionary Algorithm, Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, Aug. 30–Sept. 1, AIAA-2004-4323, 2004
[Ling2006] Qing L., Gang W., Zaiyue Y., Qiuping W., Robust Optimal Design Under Standard Crowding Differential Evolution Framework, The Sixth World Congress on Intelligent Control and Automation (WCICA 2006), vol.1, 3173-3177, 2006
[Lobb1994] Lobb D.R., Theory of concentric designs for grating spectrometers, Applied Optics, Vol.33, no.13, 2648–2658, 1994
[Lobb1997] Lobb D.R., Imaging spectrometers using concentric optics, in Imaging Spectrometry III, Descour M.R. and Shen S.S., eds., Proc. SPIE 3118, 339–347, 1997
[Lophaven2002] Lophaven S.N., Nielsen H.B., S?ndergaard J., DACE: a matlab Kriging toolbox, (2002-11-06)[2005-02-19], http://www.imm.dtu.dk/~hbn/dace/
[Lucke2007] Lucke R.L., Out-of-plane dispersion in an Offner spectrometer, Opt. Eng. 46(7),073004, 2007
[LuLJ2008] Lijun L., Aberration theory of plane-symmetric grating systems, J. Synchrotron Rad., 15, 399-410, 2008
[LuLJ2009] Lijun L., DongLiang L., Aberrations of plane-symmetric multielement optical systems, Opt. Int. J. Light Electron Opt.,doi:10.1016/j.ijleo.2009.01.016, 2009
[Luo2008a] Luo B., Zheng J., A New Methodology for Searching Robust Pareto Optimal Solutions with MOEAs, in Proceeding of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), HongKong, China, June, 580-586, 2008
[Luo2008b] Luo B., Zheng J., Efficient MOEAs with an Adaptive Sampling Technique in Searching Robust Optimal Solutions, IEEE Proceeding of the 7th World Congress on Intelligent Control and Automation ( WCICA07) . Chong qing, China, June, 117-123, 2008
[Mack2005] Mack Y., Goel T., Shyy W., Haftka R.T., Queipo N.V., Multiple surrogates for the shape optimization of bluff body-facilitated mixing, Proceedings of the 43rd AIAA aerospace sciences meeting and exhibit, Reno, NV, Paper no. 2005–0333, AIAA, 2005
[Markham1986] Markham B.L., Barker J.L., Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures, EOSAT Landsat Technical Notes, Vol.1, 3-8, 1986
[Markon2001] Markon S., Arnold D., et al., Thresholding - A Selection Operator for Noisy ES, in Proceeding of Congress on Evolutionary Computation, 465-472, 2001
[Masui1999a] Masui S., Namioka T., Geometric aberration theory of double-element optical systems, JOSA A, 16(9):2253, 1999
[Masui1999b] Masui S., Namioka T., Aberration theory and a design method of double-element optical systems, in Proceedings of SPIE, Vol.3737, 498-508, 1999
[Matheron1963] Matheron G., Principles of geostatistics, Economic Geology, vol.58, no.8, 1246-1266, 1963
[Mattson2003] Mattson C., Messac A., Handling equality constraints in robust design optimization, Structures, Structural Dynamics, and Materials Conference 2003, 1-10, 2003
[Mckay1979] Mckay M.D., Beckman R.J., Conover W.J., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, no.2, 239-245, 1979
[Milliken2008] Milliken R.E., et al., Opaline silica in young deposits on mars, Geology, 36, 847– 850, doi:10.1130/G24967A.1.,2008
[Morozumi1979] Morozumi S., Aberration theory of diffraction gratings, Optik, vol.53, no.2, 75-88, 1979
[Morozumi1984] Morozumi S., Toric-wavefront testing holographic interferometry for concave grating, Applied Optics, vol.23, no.18, 3082-3090, 1984
[Mouroulis2000] Mouroulis P., McKerns M., Pushbroom imaging spectrometer with high spectroscopic data fidelity: experimental demonstration, Opt. Eng. 39, 808-816, 2007
[Mouroulis2007] Mouroulis P., Sellar R.G., Wilson D.W., Shea J.J., Green R.O., Optical design of a compact imaging spectrometer for planetary mineralogy, Opt. Eng. 46(6), 063001, 2007
[Murchie2007] Murchie S., Arvidson R., Beisser K., Bibring J.P., Bishop J., Boldt J., Bussey B., Choo T., Clancy R.T., Darlington E.H., Des Marais D., Fasold M., Fort D., Green R., Guinness E., Hayes J., Heyler G., Humm D., Lee R., Lees J., Lohr D., Malaret E.,Morris R., Mustard J., Rhodes E., Robinson M., Roush T., Schaefer E., Seagrave G., Silvergate P., Smith M., Strohbehn K., Thompson P., Tossman B., Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO), 112, E05S03, 2007
[Murty1960] Murty M., Spherical zone plate diffraction grating, Journal of Optical Society of America, vol.50, no.9, 923-923, 1960
[Mustard2008] Mustard J.F., et al., Hydrated silicate minerals on Mars observed by the CRISM instrument on MRO, Nature, 454, 305– 309, 2008
[Noda1974a] Noda H., Namioka T., Seya M., Geometric theory of the grating, J. Opt. Soc. Am., 64: 1031-1036, 1974
[Noda1974b] Noda H., Namioka T., Seya M., Design of holographic concave gratings for Seya-Namioka monochromators, J. Opt. Soc. Am., 64: 1042-1048, 1974
[Noda1974c] Noda H., Namioka T., Seya M., Ray tracing through holographic gratings, J. Opt. Soc. Am., 64: 1037-1042, 1974
[Namioka1959a] Namioka T., Theory of the concave grating. I, J. Opt. Soc. Am, 49, 446-460, 1959
[Namioka1959b] Namioka T., Theory of the Concave Grating. II. Application of the Theory to the Off-Plane Eagle Mounting in a Vacuum Spectrograph, J. Opt. Soc. Am, 49, 461-465, 1959
[Namioka1961a] Namioka T., Theory of the ellipsoidal concave grating II, J Opt. Soc. Am, 51, 13-16, 1961
[Namioka1961b] Namioka T, Theory of the ellipsoidal concave grating I, J. Opt. Soc. Am, 51, 4-12, 1961
[Namioka1976] Namioka T., Seya M., Noda H., Design and performance of holographic concave gratings, Japanese Journal of Applied Physics, 15, 1181-1197, 1976
[Namioka1992] Namioka T., Koike M., Analytical representation of spot diagrams and its application to the design of monochromators, Nucl. Instrum. Methods A, 139:219-227, 1992
[Namioka1994] Namioka T., Koike M., Content D., Geometric theory of the ellipsoidal grating, Appl. Opt., 33:7261–7274, 1994
[Namioka1995] Namioka T., Koike M., Aspheric wave-front recording optics for holographic gratings, Appl. Opt., Vol.34, no.13, 2180-2186, 1995
[Offner1973] Offner A., Unit power imaging catoptric anastigmat, U. S. Patent No. 3,748,015, 1973
[Ong2006] Ong Y., Nair P., Lum K., Max-min surrogate assisted evolutionary algorithm for robust design, IEEE Trans. Evolution. Comput., vol.10, no.4, 392-404, 2006
[Palmer1998a] Palmer C., McKinney W., Wheeler B., Imaging equations for spectroscopic systems using Lie transformations. Part I Theoretical Foundations, Proc. SPIE 3450, 55-66, 1998
[Palmer1998b] Palmer C., McKinney W., Wheeler B., Imaging equations for spectroscopic systems using Lie transformations. Part II—Multi-element systems, Proc. SPIE, 3450, 67, 1998
[Pearlman2001] Pearlman J., Carman S., Segal C., Jarecke P., Barry P., Rowne W., Overview of the Hyperion imaging spectrometer for the NASA EO-1 mission, in Proc. IGARSS, 6, 3504–3506, 2001
[Pearlman2003] Pearlman J., Barry P.S., Segal C.C., Shepanski J., Beiso D., Carman S.L., Hyperion, a space-based imaging spectrometer, IEEE Transactions on Geoscience and Remote Sensing , 41, 1160–1173, 2003
[Petrowski1996] Petrowski A., A clearing procedure as a niching method for genetic algorithms, in Proceeding of IEEE International Conference on Evolutionary Computation, 798-803, 1996
[Pfeiffer2008] Pfeiffer J., Golle U., Rothlauf F., Reference point based multi-objective evolutionary algorithms for group decisions, In Proceedings of the 10th annual conference on Genetic and evolutionary computation(GECCO’08), 697-704, 2008
[Pieters2009] Pieters C.M., et al., Character and spatial distribution of OH/H2O on the surface of the Moon seen by M3 on Chandrayaan-1. Science, 326, 568, 2009
[Prieto-Blanco2006] Prieto-Blanco X., Montero-Orille C., Couce B., de la Fuente R., Analyticaldesign of an Offner imaging spectrometer, Optics Express, 14(20), 9156-9168, 2006
[Prieto-Blanco2009] Prieto-Blanco X., Montero-Orille C., González-Nú?ez H., Mouriz M.D., López Lago E., de la Fuente R., Imaging with classical spherical diffraction gratings: the quadrature configuration, J. Opt. Soc. Am. A 26(11), 2400–2409, 2009
[Prieto-Blanco2010] Prieto-Blanco X., Montero-Orille C., González-Nú?ez H., Mouriz M.D., López Lago E., de la Fuente R., The Offner imaging spectrometer in quadrature, 18(12), 12756–12769, 2010
[Queipo2005] Queipo N.V., Haftka R.T., Shyy W., Goel T., Vaidyanathan R., Kevin Tucker P., Surrogate-based analysis and optimization, Progress in Aerospace Sciences, Vol. 41, no. 1, 1-28, 2005
[Reininger1994] Reininger F.M., Dami M., Paolinetti R., Pieri S., Falugiani S., Visible infrared mapping spectrometer–visible channel (VIMS-V), in Instrumentation in Astronomy VIII, Crane E.R. and Crawford D.L., eds., Proc. SPIE 2198, 239–250, 1994
[Reininger1996] Reininger F.M., Coradini A., Capaccioni F., Capria M.T., Cerroni P., De Sanctis M.C., Magni G., Drossart P., Barucci M.A., Bockelee-Morvan D., Combes J.M., Crovisier J.M., Encrenaz T., Reess J., Semery A., Tiphene D., Arnold G., Carsenty U., Michaelis H., Mottola S., Neukum G., Peter G., Schade U., Taylor F.W., Calcutt S.B., Vellacott T., Venters P., Watkins R.E., Bellucci G., Formisano V., Angrilli F., Bianchini G., Saggin B., Bussoletti E., Colangeli L., Mennella V., Fonti S., Bibring J.P., Langevin Y., Schmitt B., Combi M., Fink U., McCord T.B., Ip W., Carlson R.W., Jennings D.E.,“VIRTIS: visible infrared thermal imaging spectrometer for the Rosetta mission,”in Imaging Spectrometry II, M. R. Descour and J. M. Mooney, eds., Proc. SPIE 2819, 66–77, 1996
[Romero1991] Romero C., Handbook of Critical Issues in Goal Programming, Oxford, UK: Pergamon Press, 1991
[Rosenberg1967] Rosenberg R.S., Simulation of genetic populations with biochemical properties
[Ph.D. Thesis], Michigan: University of Michigan, 1967
[Rosenkranz1993] Rosenkranz P.W., Staelin D.H., Atmospheric infrared sounder, Massachusetts Inst. of Tech. Report, vol.1, 1993
[Rowland1883] Rowland H.A., On Concave Gratings for Optical Purposes, Phil. Mag., 16, 197-210, 1883
[Rudolph2000] Rudolph G., Agapie A., Convergence properties of some multi-objective evolutionary algorithms, in Proceedings of 2000 Congress on Evolutionary Computation, vol.2, 1010-1016, 2000
[Sacks1989a] Sacks J., Welch W.J., Mitchell T.J. Wynn H.P., Design and analysis of computer experiments. Statistical Science, vol.4, 409-435, 1989
[Sacks1989b] Sacks J., Schiller S.B., Welch W.J., Design for computer experiments. Technometrics, vol.31, 41-47, 1989
[Sasena2002] Sasena M.J., Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, A Ph.D. Dissertation, University of Michigan, 2002
[Schaffer1984] Schaffer J.D., Some experiments in machine learning using vector evaluated genetic algorithms [PhD thesis], Vanderbilt University, 1984
[Schaffer1985] Schaffer J.D., Multiple objective optimization with vector evaluated genetic algorithms, In: Proceeding. of the 1st International Conference on Genetic Algorithms and Their Applications, 93?100, 1985
[Schonlau1997] Schonlau M., Welch W., Jones D., A Data-Analytic Approach to Bayesian Global Optimization, American Statistical Association Proceeding, Section of Physical Engineering Sciences, 186-191, 1997
[Shimoyama2009] Shimoyama K., Lim J.N., Jeong S., Obayashi S., Koishi M., et al., Practical Implementation of Robust Design Assisted by Response Surface Approximation and Visual Data-Mining,Journal of Mechanical Design, vol.131, 061007, 2009
[Singh1980] Singh M., Singh S., Holographic cylindrical grating for X-ray and XUV spectroscopy in grazing incidence, Applied Optics, vol.19, no.19, 3313-3317, 1980
[Singh1981] Singh M., Singh S., Holographic cylindrical grating extreme ultraviolet (EUV) spectroscopy in near normal incidence, Applied Optics, vol.20, no.1, 153-167, 1981
[Smith1996] Smith W.H., Philip D.H., Digital array scanned interferometer: sensors and results. App. Opt, Vol.35, no.16, 290222909, 1996
[Soares2009] Soares G.L., Guimaraes F.G., Maia C.A., Vasconcelos J.A., Jaulin L., Interval Robust Multi-Objective Evolutionary Algorithm, IEEE Congress on Evolutionary Computation (CEC'09), 1637-1643, 2009
[Srinivas1994] Srinivas N., Deb K., Multi-objective optimization using non-dominated sorting in genetic algorithms, Evolutionary Computation,vol.2, no.3, 221?248, 1994
[Tsutsui1999] Tsutsui S., A comparative study on the effects of adding perturbations to phenotypic parameters in genetic algorithms with a robust solution searching scheme, IEEE International Conference on Systems, Man and Cybernetics 1999, 585-591, 1999
[Van1998] Van Veldhuizen D.A., Lamont G.B., Evolutionary computation and convergence to a pareto front, Late Breaking Papers at the Genetic Programming 1998 Conference, 221-228, 1998
[Van1999] Van Veldhuizen D.A., Multi-objective evolutionary algorithms: classifications, analysis, and new innovations, PHD Thesis, Department of electrical and computer engineering, air force institute of technology, Wright-Patterson AFB, Ohio, 1999
[Van2000] Van Veldhuizen D.A., Lamont G.B., Multi-objective evolutionary algorithms: Analyzing the state-of-the-art, Evolutionary Computation Journal, vol.8, no.2, 125–148, 2000
[Van2003] Van Veldhuizen D.A., Zydallis J.B., Lamont G.B., Considerations in engineering parallel multi-objective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.7, no.2, 144-173, 2003
[Van2007] Van Dam E., Husslage B., den Hertog D., Melissen H., Maximin Latin hypercube designs in two dimensions, Operations Research, Vol.55, no.1, 158–169, 2007
[Velzer1976] Velzl C., A general theory of the aberrations of diffraction gratings and grating like optical instrument, Journal of Optical Society of America, vol.66, no.4, 346-353, 1976
[Velzer1977] Velzl C., On the imaging properties of holographic gratings, Journal of Optical Society of America, vol.67, no.8, 1021-1027, 1977
[Viana2010] Viana F.A.C., Venter G., Balabanov V., An algorithm for fast optimal Latin hypercube design of experiments, International Journal for Numerical Methods in Engineering, vol.83, no.2, 135-156, 2010
[Vining2005] Vining G.G., Kowalski S.M., Montgomery D.C., Response surface designs within a split-plot structure, Journal of Quality Technology, vol.37, no.2, 115-129, 2005
[Walter2006] Walter I., Hirsch H., Jahn H., Knollenberg J., Venus H., MERTIS– a highly integrated IR imaging spectrometer, Proc. SPIE, 6297, 62970X, 2006
[Wang2007] Wang G.G., Shan S., Review of Metamodeling Techniques in Support of Engineering Design Optimization, ASME J. Mech. Des., Vol.129, no.4, 370–380, 2007
[Werner1967] Werner W., The geometrical optical aberration theory of diffraction gratings, Applied Optics, vol.6, no.10, 1691-1699, 1967
[Wilson2001] Wilson B., Cappelleri D., Frecker M., Simpson T.W., Efficient Pareto Frontier Exploration Using Surrogate Approximations, Optim. Eng., Vol.2, 31-50, 2001
[Ye2000] Ye K.Q., Li W., Sudjianto A., Algorithmic construction of optimal symmetric Latin hypercube designs, Journal of Statistical Planning and Inference, Vol.90, 145–159, 2000
[Yen2003] Yen G.G., Lu H., Dynamic Multi-objective Evolutionary Algorithm: Adaptive Cell-Based Rank and Density Estimation, IEEE transactions on Evolutionary computation, vol.7, no.3, 253-274, 2003
[Zadeh1963] Zadeh L.A., Optimality and nonscalar-valued performance criteria, IEEE Transactions on Automatic Control, vol.8, no.1, 59-60, 1963
[Zhang2007] Zhang Q.F., Li H., MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation, vol.11, no.6, 712?731, 2007
[Zhang2010] Zhang, Q., Liu W., Tsang E., Virginas B., Expensive multi-objective optimization by MOEA/D with Gaussian process model, IEEE Transactions on Evolutionary Computation, vol.14, no.3, 456-474, 2010
[Zitzler1999] Zitzler E., Thiele L., Multi-Objective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Trans. on Evolutionary Computation, vol.3, no.4, 257?271, 1999
[Zitzler2000] Zitzler E., Deb K., Thiele L., Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation Journal, vol.8, no. 2, 173–195, 2000
[Zitzler2002] Zitzler E., Laumanns M., Thiele L., SPEA2: Improving the strength Pareto evolutionary algorithm. In: Giannakoglou K., Tsahalis D.T., Périaux J., Papailiou K.D., Fogarty T., eds. Evolutionary Methods for Design, Optimization and Control with Applications to.Industrial Problems. Berlin: Springer-Verlag, 95?100, 2002
[Zhou2009] Zhou A., Zhang Q., Jin Y., Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation,vol.13, no.5, 1167-1189, 2009
[ChenYR2009]陈一仁,非致冷探测器空间应用的开发,光机电信息,Vol.26, no. 9, 1-6, 2009
[ChenQ2002]陈锵,变间距光栅刻线密度测量,中国科学技术大学硕士学位论文,2002
[CuiXX2001]崔逊学,李淼,方廷健,多目标协调进化算法研究,计算机学报,Vol.24, no.9, 1-6, 2001
[CuiXX2006]崔逊学,多目标进化算法及其应用,国防工业出版社,北京,2006
[DaiGM2009]戴光明,王茂才,多目标优化算法及其在卫星星座设计中的应用,中国地质大学出版社,武汉,2009
[GaoYH2009]高月华,基于Kriging代理模型的优化设计方法及其在注塑成形中的应用,大连理工大学博士毕业论文,2009
[GongGB2009]宫广彪,超光谱成像仪前置成像物镜设计,苏州大学硕士学位论文,2009
[GanFP2002]甘甫平,王润生,杨苏明,西藏Hyperion数据蚀变矿物识别初步研究,国土资源遥感,no.4,52-67, 2002
[HuZW2005]胡中文,广义光栅方程与光栅线密度测试及二维CCD全谱光谱仪的研制,中国科学技术大学博士学位论文,2005
[HuangN2006]黄楠,基于Hyperion数据的地物分类方法研究,东北林业大学硕士毕业论文,2006
[HuangYSH2005a]黄元申,倪争技,同心三反射镜光学系统研究,光学仪器,Vol.27, no.2, 42-46, 2005
[HuangYSH2005b]黄元申,倪争技,庄松林,光栅成像光谱仪同心光学系统研究,光学仪器,Vol.27, no.6, 38-42, 2005
[HuangYSH2007]黄元申,陈家璧,凸面光栅Offner结构成像光谱仪的傅里叶分析,光学仪器,Vol.29, no.6, 40-43, 2007
[HuangYSH2008]黄元申,陈南曙,张大伟,倪争技,庄松林,小型Offner光谱成像系统的设计,光学精密工程,Vol.17, no.11, 2629-2637, 2008
[LeiDM2009]雷德明,多目标智能优化算法及其应用,科学出版社,北京,2009
[TongQX2006]童庆禧,张兵,郑兰芬,高光谱遥感:原理、技术与应用,高等教育出版社,2006
[TongYJ2010a]佟亚军,吴刚,周全等,Offner成像光谱仪的设计方法,光学学报,Vol.30, no.4, 1148-1152, 2010
[TongYJ2010b]佟亚军,软X射线光栅单色器精密测试及成像光谱仪研究,中国科学院研究生院博士学位论文,2010
[WangJ2008]王娟,吕丽军,光栅波像差理论的推广,上海大学学报, Vol.14, no.2, 177-182, 2008
[YanFL2006]阎福礼,王世新,周艺,肖青,祝令亚,王丽涛,焦云清,利用Hyperion星载高光谱传感器监测太湖水质的研究,红外与毫米波学报, Vol.25, no.6, 460-464, 2006
[ZhangCHM2000]张淳民,相里斌,赵葆常,刘良云,干涉成像光谱仪技术的新发展,光学技术,Vol.26, no. 3, 232-234, 2000
[ZhaoBCH2009]赵葆常,杨建峰,常凌颖,陈立武,贺应红,薛彬,嫦娥一号卫星成像光谱仪光学系统设计与在轨评估,光子学报,Vol.38, no.3, 479-483, 2009
[ZHengJH2006]郑金华,多目标进化算法及其应用,科学出版社,北京,2006
[ZHengYQ2005]郑玉权,小型Offner光谱成像系统的设计光学精密工程,Vol.13, no.6, 650-657, 2005
[ZHouQ2010]周全,Offner田野成像光谱仪研制与图像处理,中国科学技术大学博士学位论文,2010
[Adra2007] Adra S. F., Griffin I., Fleming P. J., A comparative study of progressive preference articulation techniques for multi-objective optimization, Lecture Notes in Computer Science, 4403:908_921, 2007
[Aichholzer2002] Aichholzer O., Aurenhammer F., Brandstatter B., Ebner T., Krasser H., Magele C., Muhlmann M., Renhart W., Evolution Strategy and Hierarchical Clustering, IEEE Transactions on Magnetics, vol.38, no.2, 1041-1044, 2002
[Alba2005] Alba E., Dorronsoro B., The exploration exploitation tradeoff in dynamic cellular genetic algorithms, IEEE Transactions on Evolutionary Computation, vol.9, no.2, 126-142, 2005
[Arnold2008] Arnold G., Helbert J., Hiesinger H., Hirsch H., Jessberger E.K., Peter G., and Walter I., Mercury radiometer and thermal infrared spectrometer: a novel thermal imaging spectrometer for the exploration of Mercury, J. Appl. Rem. Sens. 2, p. 23528, 2008
[Barducci2004] Barducci A., De Cosmo V., Marcoionni P., Pippi I., ALISEO: a new stationary imaging interferometer, SPIE, 5546: 262-270, 2004
[Barrico2006a] Barrico C., Antunes C.H., Robustness Analysis in Evolutionary Multi-objective Optimization with a Case Study in Electrical Distribution Networks, II European Latin American Workshop on Engineering Systems (SELASI II), Porto, Portugal, 2006
[Barrico2006b] Barrico C., Antunes C.H., A New Approach to Robustness Analysis in Multi-objective Optimizat ion, Proceedings of the 7th International Conference on Multi-objective Programming and Goal Programming (MOPGP06), Tours, France, June 2006
[Barrico2006c] Barrico C., Antunes C.H., Robustness Analysis in Multi-objective Optimization Using a Degree of Robustness Concept, Proceedings of the 2006 IEEE Congress on Computation Intelligence (WCCI2006), Vancouver, Canada, July 2006, 6778-6783
[Benson1978] Benson H.P., Existence of efficient solutions for vector maximization problems,Journal of Optimization Theory and Applications, vol.26, no.4, 569-580, 1978
[Beutler1945] Beutler H.G., The theory of the concave grating, J. Opt. Soc. Am., 35, 311-350, 1945
[Beyer2007] Beyer H.G., Sendhoff B., Robust optimization-a comprehensive survey, Computer methods in applied mechanics and engineering, Vol.196 no.33-34, 3190-3218, 2007
[Branke1998] Branke J., Creating robust solutions by means of an evolutionary algorithm, Parallel Problem Solving from Nature, 119–128, 1998
[Branke2001] Branke J., Kau?ler T., Schmeck H., Guidance in evolutionary multi-objective optimization, Advances in Engineering Software, vol.32, no.6, 499–508, 2001
[Branke2005a] Branke J., Deb K., Integrating user preferences into evolutionary multi-objective optimization, Knowledge Incorporation in Evolutionary Computation, 461-477, 2005
[Branke2005b] Branke J., Schmidt C., Faster convergence by means of fitness estimation, Soft Computing, vol.9, no.1, 13-20, 2005
[Breckinridge1996] Breckinridge J.B., Evolution of imaging spectrometry: Past, present and future, SPIE, 2819: 2-6, 1996
[Brown2005] Brown R.H., Baines K.H., Bellucci G., Bibring J.P., Buratti B.J., Bussoletti E., Capaccioni F., Cerroni P., Clark R.N., Coradini A., Cruikshank D.P., Drossart P., Formisano V., Jaumann R., Langevin Y., Matson D.L., McCord T.B., Mennella V., Miller E., Nelson R.M., Nicholson P.D., Sicardy B., Sotin C., The Cassini Visual and Infrared Mapping Spectrometer Investigation. Space Sci. Rev. 115 (1–4), 111–168, 2005
[Chan2008] Chan T.M., Man K.F., Tang K.S., Kwong S., A jumping gene paradigm for evolutionary multi-objective optimization, IEEE Transactions on Evolutionary Computation, vol.12, no.2, 143-59, 2008
[Chanes1955] Chanes A., Cooper W.W., Ferguson R.O., Optimal estimation of executive compensation by linear programming, Management Science, vol.1, no.2, 138-151, 1955
[Chaudhuri2010] Chaudhuri S., Deb K., An interactive evolutionary multi-objective optimization and decision making procedure, Applied Soft Computing, vol.10, no.2, 496-511, 2010
[Chrisp1983a] Chrisp M.P., Aberrations of holographic toroidal grating systems, Applied Optics, 22: 1508-1518, 1983
[Chrisp1983b] Chrisp M.P., X-ray spectrograph design, Applied Optics, 22: 1519-1529, 1983
[Chrisp1999] Chrisp M.P., Convex diffraction grating imaging spectrometer, U.S. Patent, 5880834, 1999
[Chung2004] Chung H., Alonso J., Multi-objective Optimization Using Approximation Model-Based Genetic Algorithms, Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, Aug. 30–Sept. 1, AIAA-2004-432, 2004
[Coello2001] Coello Coello C.A., Pulido G.T., A micro-genetic algorithm for multi-objective optimization. In: Spector L., Goodman E.D., Wu A., Langdon W.B., Voigt H.M., Gen M., eds. Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2001. San Francisco: Morgan Kaufmann Publishers, 274?282, 2001
[Coello2003a] Coello Coello C.A, Becerra R.L., Evolutionary multi-objective optimization using a cultural algorithm, IEEE Swarm Intelligence Symposium, 6-13, 2003
[Coello2003b] Coello Coello C.A, Sierra M.R., A co-evolutionary multi-objective evolutionary algorithm, The 2003 Congress on Evolutionary Computation, 2003. CEC'03, 482-489, 2003
[Cone2000] Corne D.W., Knowles J.D., Oates M.J., The Pareto-envelope based selection algorithm for multi-objective optimization. In:Schoenauer M., Deb K., Rudolph G., Yao X., Lutton E., Merelo J.J., Schwefel H.P., eds. Parallel Problem Solving from Nature, PPSN VI. LNCS, Berlin: Springer-Verlag, 869?878, 2000
[Cone2001] Corne D.W., Jerram N.R., Knowles J.D., Oates M.J., PESA-II: Region-Based selection in evolutionary multi-objective optimization. In: Spector L., Goodman E.D., Wu A., Langdon W.B., Voigt H.M., Gen M., eds. Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2001, San Francisco: Morgan Kaufmann Publishers, 283?290, 2001
[Coradini2004] Coradini A., Filacchione G., Capaccioni F., Cerroni P., Adriani A., Brown R.H., Langevin Y., Gondet B., CASSINI/VIMS-V at Jupiter: radiometric calibration test and data results. Planet. Space Sci. 52, 661–670, 2004
[Cvetkovic1999] Cvetkovic D., Parmee I.C., Use of Preferences for GA_based Multi-objective Optimisation. In GECCO_99: Proceedings of the Genetic and Evolutionary Com-putation Conference, vol.2, 1504-1509, 1999
[Danielson1974] Danielson A., Lindblom P., Focusing conditions of the spherical concave grating, Optik, vol.41, no.4, 441-451, 1974
[Das1986] Das N.C., Murty M., Flat Field Spectrograph Using Convex Holographic Diffraction Grating and Concave Mirror, Pramana, 27(1-2), 171-192, 1986
[Davis1999] Davis C.O., Kappus M., Bowles J., Fisher J., Antoniades J., Carney M., Calibration, Characterization and first Results with the Ocean PHILLS Hyperspectral Imager, Proceedings of the SPIE, V. 3753, 160-168, 1999
[Davis2002] Davis C.O., et al., Ocean PHILLS hyperspectral imager: design, characterization, and calibration, Optics Express, 10(4): 210-221, 2002
[Davis2006] Davis C.O., Carder K.L., Gao B.C., Lee Z.P., Bissett W.P., The development of imaging spectrometry of the coastal ocean, IEEE International Conference on Geoscience and Remote Sensing Symposium 2007, 1982—1985, 2007
[Deb1999] Deb K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evolutionary Computation Journal, vol.7, no.3, 205–230, 1999
[Deb2001a] Deb K., Multi-Objective optimization using evolutionary algorithms, Chicester, UK: John Wiley & Sons, 2001
[Deb2001b] Deb K., Pratap A., Meyarivan T., Constrained test problems for multi-objective evolutionary optimization. Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, 284–298, 2001
[Deb2002a] Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. On Evolutionary Computation, vol.6, no.2, 182?197, 2002
[Deb2002b] Deb K., Thiele L., Laumanns M., Zitzler E., Scalable multi-objective optimization test problems, Proc. Congr. Evol. Comput. -CEC’02, vol. 1, Piscataway, New Jersey, 825–830, 2002.
[Deb2005a] Deb K., Mohan M., Mishra S., Evaluating theε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions, Evolutionary Computation, vol.13, no. 4, 501-525, 2005
[Deb2005b] Deb K., Saxena D.K., On finding Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems, Kangal report 2005011, KanGAL, Indian institute of Technology, India, 2005
[Deb2005c] Deb K., Thiele L., Laumanns M., Zitzler E., Scalable test problems for evolutionary multi-objective optimization, Evolutionary Multi-objective Optimization, 105-145, 2005
[Deb2005d] Deb K, Gupta H., Searching for Robust Pareto optimal Solutions in Mult i-Objective Optimization, Proceedings of the Third International Conference on Evolutionary Multi-criterion Optimization (EMO2005), Guanajuato, Mexico, Lecture Notes on Computer Science, 150-164, 2005
[Deb2006] Deb K., Sundar J., Uday N., Chaudhuri S., Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms, International Journal of Computational Intelligence Research (IJCIR), vol.2, no.6, 273-286, 2006
[Deb2007] Deb K., Kumar A., Interactive evolutionary multi-objective optimization and decision-making using reference direction method, In: Proceedings of the Genetic andEvolutionary Computation Conference (GECCO-2007), New York: The Association of Computing Machinery (ACM), 781-788, 2007
[Demarez1999] Demarez V., Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model, International Journal of Remote Sensing, vol.20, no.5, 879-894, 1999
[Duban1991] Duban M., Third-generation Rowland holographic mounting, Applied Optics, vol.30, no.28, 4019-4025, 1991
[Duban1999A] Duban M., Theory and computation of three Cosmic Origin Spectrograph aspheric gratings recorded with a multimode deformable mirror, Applied Optics, vol.38, no.7, 1096-1102, 1999
[Duban1999B] Duban M., Third-generation holographic Rowland mounting: fourth-order theory, Applied Optics, vol.38, no.16, 3443-3449, 1999
[Elvidge1990] Elvidge C.D., Chen Z., Groeneveld D.P., Detection of trace quantities of green vegetation in 1990 AVIRIS data, Remote Sensing of Environment, vol.44,no.2-3, 271-279, 1993
[Erickson2001] Erickson M., Mayer A., Horn J., The niched Pareto genetic algorithm 2 applied to the design of groundwater remediation system. In: Zitzler E., Deb K., Thiele L., Coello Coello C.A., Corne D., eds. Proc. of the 1st Int’l Conf. on Evolutionary Multi-Criterion Optimization, EMO 2001. Berlin: Springer-Verlag, 681?695, 2001
[Ehlmann2008] Ehlmann B.L., et al., Orbital identification of carbonate-bearing rocks on Mars, Science, 322, 1828– 1832, doi:10.1126/science.1164759, 2008
[Fang2004] Fang H., Rais-Rohani M., Horstemeyer M., Multiobjective Crashworthiness Optimization With Radial Basis Functions, Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, Aug. 30–Sept. 1, AIAA-2004-4487, 2004
[Farina2002] Farina M., A Neural Network Based Generalized Response Surface Multiobjective Evolutionary Algorithm, Proceedings of Congress on Evolutionary Computation, IEEE, New York, 956–961, 2002.
[Fonseca1993] Fonseca C.M., Fleming P.J., Genetic algorithm for multi-objective optimization, Formulation, discussion and generation. In: Forrest S, ed. Proc. of the 5th Int’l Conf. on Genetic Algorithms, San Mateo: Morgan Kauffman Publishers, 416?423, 1993
[Geoffrion1968] Geoffrion A.M., Proper efficiency and the theory of vector optimization, Journal of Mathematical Analysis and Application, vol.41, 491-502, 1968
[Goel2005] Goel T., Mack Y., Haftka R.T., Shyy W., Queipo N.V., Interaction between grid and design space refinement for bluff body-facilitated mixing, Proceedings of the 43rd AIAA aerospace sciences meeting and exhibit, Reno, NV, Paper no. 2005–0125, AIAA, 2005
[Goldberg1989] Goldberg D.E., Genetic algorithm for search, optimization, and machine learning, Boston: Addison-Wesley Longman Publishing Co., Inc., 1989
[Goto1993] Goto K, Kurosaki T., Canonical formation for the geometrical optics of concave gratings, J Opt Soc Am, A, 10, 452-465, 1993
[Green1998] Green R.O., Eastwood M.L., Sarture C.M., Chrien T.G., Aronsson M., Chippendale B.J., Faust J.A., Pavri B.E., Chovit C.J., Solis M., Olah M.R., Williams O.,“Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),”Remote Sens. Environ. 65, 227–248, 1998
[Green2007]Green R.O., Asner G., Ungar S., Knox R., NASA Mission to Measure Global Plant Physiology and Functional Types. Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA,2007
[Green2008] Green R.O., Pieters C.M., Mouroulis P., Sellar G., Eastwood M., Geier S., Shea J., et al., Calibration, shipment and initial spacecraft integration of the Moon Mineralogy Mapper (M3) imaging spectrometer for the Chandrayaan-1 mission, Lunar and Planetary Institute Science Conference Abstracts, 39,1803, 2008
[Grosso2008] Grosso A., Jamali A., Locatelli M., Finding maximin Latin hypercube designs by iterated local search heuristics, European Journal of Operational Research, vol.197, no.2, 541–547, 2008
[Gunawan2004] Gunawan S., Azarm S., Non-gradient based parameter sensitivity estimation for single objective robust design optimization, ASME Journal of Mechanical Design, vol.126, 395-402, 2004
[Gupta2005] Gupta H., Deb K., Handling constraints in robust multi-objective optimization, In IEEE Congress on Evolutionary Computation (CEC-2005), Edinburgh, U. K, vol.1, 25-32, 2005
[Haber1950] Haber H., Torus grating, J. Opt. Soc. Am., 40: 153-165, 1950
[Harada1999] Harada T., Takahashi K., Sakuma H., Osyczka A., Optimum design of agrazing-incidence flat-field spectrograph with a spherical varied-line-space grating, Applied Optics, vol.38, no.13, 2743-2748, 1999
[Haimes1971] Haimes Y.Y., Lasdon L.S., Wismer D.A., On a bicriterion formulation of the problems of integrated systems identification and system optimization, IEEE Transactions System, Man, Cybernetics, vol.1, no.3, 296-297, 1971
[Hajela1992] Hajela P., Lin C.Y., Genetic search strategies in multicriterion optimal design, Structural Optimization, Vol.4, 99-107, 1992
[Hanne1999] Hanne T., On the convergence of multi-objective evolutionary algorithms, European Journal of Operational Research, vol.117, no.3, 553-564, 1999
[Harada1980] Harada T., Kita T., Mechanically ruled aberration-corrected concave gratings, Applied Optics, vol.19, no.23, 3987-3993, 1980
[Hettrick1983] Hettrick M., Bowyer S., Variable line-space gratings: new designs for use in grazing incidence spectrometers, Applied Optics, vol.22, no.24, 3921-3924, 1983
[Hettrick1984] Hettrick M., Aberration of varied line-space grazing incidence gratings in converging light beams, Applied Optics, vol.23, no.18, 3221-3235, 1984
[Hirsch 2004] Hirsch H., Optical design of MERTIS in BepiColombo MERTIS-TIS, ESA-ref. MER-IFP_PP-001, 32-35, 2004
[Horn1994] Horn J., Nafpliotis N., Goldberg D.E., A niched Pareto genetic algorithm for multi-objective optimization, In: Fogarty TC, ed. Proc. Of the 1st IEEE Congress on Evolutionary Computation, Piscataway: IEEE, 82?87, 1994
[Huband2006] Huband S., Hingston P., Barone L., While L., A review of multi-objective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., vol. 10, no. 5, 477–506, 2006
[Igel2007] Igel C., Hansen N., Roth S., Covariance matrix adaptation for multi-objective optimization, Evolutionary Computation, Vol.15, no.1, 1-28, 2007
[Ishiguro1979] Ishiguro E., Iwanaga R., Oshio T., Geometrical optical theory of diffraction grating, Journal of Optical Society of America, vol.69, no.11, 1530-1538, 1979
[Ishibuchi2003] Ishibuchi H., Yoshida T., Murata T., Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling, IEEE Transactions on Evolutionary Computation, vol.7, no.2, 204-223, 2003
[Jaumann2006] Jaumann R., Stephan K., Brown R.H., McCord T.B., Coradini A., Capaccioni F., Filacchione G., Clark R.N., Cerroni P., Baines K.H., Bellucci G., Bibring J.P., Buratti B.J., Bussoletti E., Combes M., Cruikshank D.P., Drossart P., Formisano V., Langevin Y., Matson D.L., Nelson R.M., Nicholson P.D., Sicardy B., Sotin C., Roatsch T., Scholten F., Matz K.D., High resolution Cassini-VIMS mosaics of Titan and the icy saturnian satellites. Planet. Space Sci., 54, 1146-1155, 2006
[Jiao2005]Jiao L.C., Gong M.G., Shang R.H., Du H.F., Lu B., Clonal selection with Immune dominance and energy based multi-objective optimization. In: Coello Coello C.A., Aguirre A.H., Zitzler E., eds. Proc. of the 3rd Int’l Conf. on Evolutionary Multi-Criterion Optimization, EMO 2005. Berlin: Springer-Verlag, 2005. 474?489.
[Jin2003] Jin Y., Sendhoff B., Trade-off between performance and robustness: An evolutionary multi-objective approach. In EMO2003, 237–251, 2003
[Jin2005a] Jin Y., A Comprehensive Survey of Fitness Approximation in Evolutionary Computation, Soft Comput., Vol.9, no.1, 3–12, 2005
[Jin2005b] Jin Y., Branke J., Evolutionary optimization in uncertain environments-A survey, IEEE Transactions on Evolutionary Computation, vol.9, no.3, 303- 317, 2005
[Johnson2004] Johnson W.R., Wilson D.W., Bearman G., Backlund J., An all-reflective computed tomography imaging spectrometer, Proc. SPIE 5660, 88 , 2004
[Johnson2005] Johnson W.R., Wilson D.W., Bearman G., All-reflective snapshot hyperspectral imager for ultraviolet and infrared applications. Opt. Lett. 30, 1464–1466, 2005
[Johnson2007] Johnson W.R., Wilson D.W., Fink W., Humayun M., Bearman G., Snapshot hyperspectral imaging in ophthalmology, Journal of Biomedical Optics, 12, 014036, 2007
[Jones1998] Jones D.R., Schonlau M., Welch W.J., Efficient global optimization of expensive black-box functions, Journal of Global 0ptimization, vol.13, 445-492, 1998
[Joseph2007] Joseph S., Kang H., Chakraborty U., Optical Design with Epsilon-Dominated Multi-objective Evolutionary Algorithm, Adaptive and Natural Computing Algorithms, 77-84, 2007
[Knowles2000] Knowles J.D., Corne D.W., Approximating the non-dominated front using the Pareto archived evolution strategy, Evolutionary Computation, vol.8, no.2, 149?172, 2000
[Knowles2003] Knowles J., Corne D., Properties of an adaptive archiving algorithm for storing nondominated vectors, IEEE Transactions on Evolutionary Computation, vol.7, no.2, 100-116, 2003
[Kwo1987] Kwo D., Lawrence G.L., Chrisp M., Design of a grating spectrometer from a 1:1 Offner mirror system, SPIE, 818, 275-279, 1987
[Laumanns2002a] Laumanns M., Ocenasek J., Bayesian optimization algorithms for multi-objective Optimization. In: Merelo J.J., Adamidis P., Beyer H.G., eds. Proc. of the 7th Int’l Conf. on Parallel Problem Solving from Nature. London: Springer-Verlag, 298?307, 2002
[Laumanns2002b] Laumanns M., Thiele L., Deb K., Zitzler E., Combining convergence and diversity in evolutionary multi-objective optimization, Evolutionary Computation, vol.10, no.3, 263-282, 2002
[Li2003] Li X. A non-dominated sorting particle swarm optimizer for multi-objective optimization. In: Cantú-Paz E., Foster J.A., Deb K., Lawrence D., Roy R., eds. Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2003. Berlin: Springer-Verlag, 37?48, 2003
[Li2006] Li M., Azarm S., Boyars A., A New Deterministic Approach Using Sensitivity Region Measures for Multi-objective Robust and Feasibility Robust Design Optimization, ASME J. Mech. Des., vol.128, no.4, 874–883, 2006
[Li2008] Li M., Li G., Azarm S., A Kriging metamodel assisted multi-objective genetic algorithm for design optimization, Journal of Mechanical Design, Vol.130, 031401, 2008
[Li2009] Li H., Zhang Q., Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Transactions on Evolutionary Computation, vol.13, no.2, 184-302, 2009
[Lian2004] Lian Y., Liou M., Multiobjective Optimization Using Coupled Response Surface Model and Evolutionary Algorithm, Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, Aug. 30–Sept. 1, AIAA-2004-4323, 2004
[Ling2006] Qing L., Gang W., Zaiyue Y., Qiuping W., Robust Optimal Design Under Standard Crowding Differential Evolution Framework, The Sixth World Congress on Intelligent Control and Automation (WCICA 2006), vol.1, 3173-3177, 2006
[Lobb1994] Lobb D.R., Theory of concentric designs for grating spectrometers, Applied Optics, Vol.33, no.13, 2648–2658, 1994
[Lobb1997] Lobb D.R., Imaging spectrometers using concentric optics, in Imaging Spectrometry III, Descour M.R. and Shen S.S., eds., Proc. SPIE 3118, 339–347, 1997
[Lophaven2002] Lophaven S.N., Nielsen H.B., S?ndergaard J., DACE: a matlab Kriging toolbox, (2002-11-06)[2005-02-19], http://www.imm.dtu.dk/~hbn/dace/
[Lucke2007] Lucke R.L., Out-of-plane dispersion in an Offner spectrometer, Opt. Eng. 46(7),073004, 2007
[LuLJ2008] Lijun L., Aberration theory of plane-symmetric grating systems, J. Synchrotron Rad., 15, 399-410, 2008
[LuLJ2009] Lijun L., DongLiang L., Aberrations of plane-symmetric multielement optical systems, Opt. Int. J. Light Electron Opt.,doi:10.1016/j.ijleo.2009.01.016, 2009
[Luo2008a] Luo B., Zheng J., A New Methodology for Searching Robust Pareto Optimal Solutions with MOEAs, in Proceeding of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), HongKong, China, June, 580-586, 2008
[Luo2008b] Luo B., Zheng J., Efficient MOEAs with an Adaptive Sampling Technique in Searching Robust Optimal Solutions, IEEE Proceeding of the 7th World Congress on Intelligent Control and Automation ( WCICA07) . Chong qing, China, June, 117-123, 2008
[Mack2005] Mack Y., Goel T., Shyy W., Haftka R.T., Queipo N.V., Multiple surrogates for the shape optimization of bluff body-facilitated mixing, Proceedings of the 43rd AIAA aerospace sciences meeting and exhibit, Reno, NV, Paper no. 2005–0333, AIAA, 2005
[Markham1986] Markham B.L., Barker J.L., Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures, EOSAT Landsat Technical Notes, Vol.1, 3-8, 1986
[Markon2001] Markon S., Arnold D., et al., Thresholding - A Selection Operator for Noisy ES, in Proceeding of Congress on Evolutionary Computation, 465-472, 2001
[Masui1999a] Masui S., Namioka T., Geometric aberration theory of double-element optical systems, JOSA A, 16(9):2253, 1999
[Masui1999b] Masui S., Namioka T., Aberration theory and a design method of double-element optical systems, in Proceedings of SPIE, Vol.3737, 498-508, 1999
[Matheron1963] Matheron G., Principles of geostatistics, Economic Geology, vol.58, no.8, 1246-1266, 1963
[Mattson2003] Mattson C., Messac A., Handling equality constraints in robust design optimization, Structures, Structural Dynamics, and Materials Conference 2003, 1-10, 2003
[Mckay1979] Mckay M.D., Beckman R.J., Conover W.J., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, no.2, 239-245, 1979
[Milliken2008] Milliken R.E., et al., Opaline silica in young deposits on mars, Geology, 36, 847– 850, doi:10.1130/G24967A.1.,2008
[Morozumi1979] Morozumi S., Aberration theory of diffraction gratings, Optik, vol.53, no.2, 75-88, 1979
[Morozumi1984] Morozumi S., Toric-wavefront testing holographic interferometry for concave grating, Applied Optics, vol.23, no.18, 3082-3090, 1984
[Mouroulis2000] Mouroulis P., McKerns M., Pushbroom imaging spectrometer with high spectroscopic data fidelity: experimental demonstration, Opt. Eng. 39, 808-816, 2007
[Mouroulis2007] Mouroulis P., Sellar R.G., Wilson D.W., Shea J.J., Green R.O., Optical design of a compact imaging spectrometer for planetary mineralogy, Opt. Eng. 46(6), 063001, 2007
[Murchie2007] Murchie S., Arvidson R., Beisser K., Bibring J.P., Bishop J., Boldt J., Bussey B., Choo T., Clancy R.T., Darlington E.H., Des Marais D., Fasold M., Fort D., Green R., Guinness E., Hayes J., Heyler G., Humm D., Lee R., Lees J., Lohr D., Malaret E.,Morris R., Mustard J., Rhodes E., Robinson M., Roush T., Schaefer E., Seagrave G., Silvergate P., Smith M., Strohbehn K., Thompson P., Tossman B., Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO), 112, E05S03, 2007
[Murty1960] Murty M., Spherical zone plate diffraction grating, Journal of Optical Society of America, vol.50, no.9, 923-923, 1960
[Mustard2008] Mustard J.F., et al., Hydrated silicate minerals on Mars observed by the CRISM instrument on MRO, Nature, 454, 305– 309, 2008
[Noda1974a] Noda H., Namioka T., Seya M., Geometric theory of the grating, J. Opt. Soc. Am., 64: 1031-1036, 1974
[Noda1974b] Noda H., Namioka T., Seya M., Design of holographic concave gratings for Seya-Namioka monochromators, J. Opt. Soc. Am., 64: 1042-1048, 1974
[Noda1974c] Noda H., Namioka T., Seya M., Ray tracing through holographic gratings, J. Opt. Soc. Am., 64: 1037-1042, 1974
[Namioka1959a] Namioka T., Theory of the concave grating. I, J. Opt. Soc. Am, 49, 446-460, 1959
[Namioka1959b] Namioka T., Theory of the Concave Grating. II. Application of the Theory to the Off-Plane Eagle Mounting in a Vacuum Spectrograph, J. Opt. Soc. Am, 49, 461-465, 1959
[Namioka1961a] Namioka T., Theory of the ellipsoidal concave grating II, J Opt. Soc. Am, 51, 13-16, 1961
[Namioka1961b] Namioka T, Theory of the ellipsoidal concave grating I, J. Opt. Soc. Am, 51, 4-12, 1961
[Namioka1976] Namioka T., Seya M., Noda H., Design and performance of holographic concave gratings, Japanese Journal of Applied Physics, 15, 1181-1197, 1976
[Namioka1992] Namioka T., Koike M., Analytical representation of spot diagrams and its application to the design of monochromators, Nucl. Instrum. Methods A, 139:219-227, 1992
[Namioka1994] Namioka T., Koike M., Content D., Geometric theory of the ellipsoidal grating, Appl. Opt., 33:7261–7274, 1994
[Namioka1995] Namioka T., Koike M., Aspheric wave-front recording optics for holographic gratings, Appl. Opt., Vol.34, no.13, 2180-2186, 1995
[Offner1973] Offner A., Unit power imaging catoptric anastigmat, U. S. Patent No. 3,748,015, 1973
[Ong2006] Ong Y., Nair P., Lum K., Max-min surrogate assisted evolutionary algorithm for robust design, IEEE Trans. Evolution. Comput., vol.10, no.4, 392-404, 2006
[Palmer1998a] Palmer C., McKinney W., Wheeler B., Imaging equations for spectroscopic systems using Lie transformations. Part I Theoretical Foundations, Proc. SPIE 3450, 55-66, 1998
[Palmer1998b] Palmer C., McKinney W., Wheeler B., Imaging equations for spectroscopic systems using Lie transformations. Part II—Multi-element systems, Proc. SPIE, 3450, 67, 1998
[Pearlman2001] Pearlman J., Carman S., Segal C., Jarecke P., Barry P., Rowne W., Overview of the Hyperion imaging spectrometer for the NASA EO-1 mission, in Proc. IGARSS, 6, 3504–3506, 2001
[Pearlman2003] Pearlman J., Barry P.S., Segal C.C., Shepanski J., Beiso D., Carman S.L., Hyperion, a space-based imaging spectrometer, IEEE Transactions on Geoscience and Remote Sensing , 41, 1160–1173, 2003
[Petrowski1996] Petrowski A., A clearing procedure as a niching method for genetic algorithms, in Proceeding of IEEE International Conference on Evolutionary Computation, 798-803, 1996
[Pfeiffer2008] Pfeiffer J., Golle U., Rothlauf F., Reference point based multi-objective evolutionary algorithms for group decisions, In Proceedings of the 10th annual conference on Genetic and evolutionary computation(GECCO’08), 697-704, 2008
[Pieters2009] Pieters C.M., et al., Character and spatial distribution of OH/H2O on the surface of the Moon seen by M3 on Chandrayaan-1. Science, 326, 568, 2009
[Prieto-Blanco2006] Prieto-Blanco X., Montero-Orille C., Couce B., de la Fuente R., Analyticaldesign of an Offner imaging spectrometer, Optics Express, 14(20), 9156-9168, 2006
[Prieto-Blanco2009] Prieto-Blanco X., Montero-Orille C., González-Nú?ez H., Mouriz M.D., López Lago E., de la Fuente R., Imaging with classical spherical diffraction gratings: the quadrature configuration, J. Opt. Soc. Am. A 26(11), 2400–2409, 2009
[Prieto-Blanco2010] Prieto-Blanco X., Montero-Orille C., González-Nú?ez H., Mouriz M.D., López Lago E., de la Fuente R., The Offner imaging spectrometer in quadrature, 18(12), 12756–12769, 2010
[Queipo2005] Queipo N.V., Haftka R.T., Shyy W., Goel T., Vaidyanathan R., Kevin Tucker P., Surrogate-based analysis and optimization, Progress in Aerospace Sciences, Vol. 41, no. 1, 1-28, 2005
[Reininger1994] Reininger F.M., Dami M., Paolinetti R., Pieri S., Falugiani S., Visible infrared mapping spectrometer–visible channel (VIMS-V), in Instrumentation in Astronomy VIII, Crane E.R. and Crawford D.L., eds., Proc. SPIE 2198, 239–250, 1994
[Reininger1996] Reininger F.M., Coradini A., Capaccioni F., Capria M.T., Cerroni P., De Sanctis M.C., Magni G., Drossart P., Barucci M.A., Bockelee-Morvan D., Combes J.M., Crovisier J.M., Encrenaz T., Reess J., Semery A., Tiphene D., Arnold G., Carsenty U., Michaelis H., Mottola S., Neukum G., Peter G., Schade U., Taylor F.W., Calcutt S.B., Vellacott T., Venters P., Watkins R.E., Bellucci G., Formisano V., Angrilli F., Bianchini G., Saggin B., Bussoletti E., Colangeli L., Mennella V., Fonti S., Bibring J.P., Langevin Y., Schmitt B., Combi M., Fink U., McCord T.B., Ip W., Carlson R.W., Jennings D.E.,“VIRTIS: visible infrared thermal imaging spectrometer for the Rosetta mission,”in Imaging Spectrometry II, M. R. Descour and J. M. Mooney, eds., Proc. SPIE 2819, 66–77, 1996
[Romero1991] Romero C., Handbook of Critical Issues in Goal Programming, Oxford, UK: Pergamon Press, 1991
[Rosenberg1967] Rosenberg R.S., Simulation of genetic populations with biochemical properties
[Ph.D. Thesis], Michigan: University of Michigan, 1967
[Rosenkranz1993] Rosenkranz P.W., Staelin D.H., Atmospheric infrared sounder, Massachusetts Inst. of Tech. Report, vol.1, 1993
[Rowland1883] Rowland H.A., On Concave Gratings for Optical Purposes, Phil. Mag., 16, 197-210, 1883
[Rudolph2000] Rudolph G., Agapie A., Convergence properties of some multi-objective evolutionary algorithms, in Proceedings of 2000 Congress on Evolutionary Computation, vol.2, 1010-1016, 2000
[Sacks1989a] Sacks J., Welch W.J., Mitchell T.J. Wynn H.P., Design and analysis of computer experiments. Statistical Science, vol.4, 409-435, 1989
[Sacks1989b] Sacks J., Schiller S.B., Welch W.J., Design for computer experiments. Technometrics, vol.31, 41-47, 1989
[Sasena2002] Sasena M.J., Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, A Ph.D. Dissertation, University of Michigan, 2002
[Schaffer1984] Schaffer J.D., Some experiments in machine learning using vector evaluated genetic algorithms [PhD thesis], Vanderbilt University, 1984
[Schaffer1985] Schaffer J.D., Multiple objective optimization with vector evaluated genetic algorithms, In: Proceeding. of the 1st International Conference on Genetic Algorithms and Their Applications, 93?100, 1985
[Schonlau1997] Schonlau M., Welch W., Jones D., A Data-Analytic Approach to Bayesian Global Optimization, American Statistical Association Proceeding, Section of Physical Engineering Sciences, 186-191, 1997
[Shimoyama2009] Shimoyama K., Lim J.N., Jeong S., Obayashi S., Koishi M., et al., Practical Implementation of Robust Design Assisted by Response Surface Approximation and Visual Data-Mining,Journal of Mechanical Design, vol.131, 061007, 2009
[Singh1980] Singh M., Singh S., Holographic cylindrical grating for X-ray and XUV spectroscopy in grazing incidence, Applied Optics, vol.19, no.19, 3313-3317, 1980
[Singh1981] Singh M., Singh S., Holographic cylindrical grating extreme ultraviolet (EUV) spectroscopy in near normal incidence, Applied Optics, vol.20, no.1, 153-167, 1981
[Smith1996] Smith W.H., Philip D.H., Digital array scanned interferometer: sensors and results. App. Opt, Vol.35, no.16, 290222909, 1996
[Soares2009] Soares G.L., Guimaraes F.G., Maia C.A., Vasconcelos J.A., Jaulin L., Interval Robust Multi-Objective Evolutionary Algorithm, IEEE Congress on Evolutionary Computation (CEC'09), 1637-1643, 2009
[Srinivas1994] Srinivas N., Deb K., Multi-objective optimization using non-dominated sorting in genetic algorithms, Evolutionary Computation,vol.2, no.3, 221?248, 1994
[Tsutsui1999] Tsutsui S., A comparative study on the effects of adding perturbations to phenotypic parameters in genetic algorithms with a robust solution searching scheme, IEEE International Conference on Systems, Man and Cybernetics 1999, 585-591, 1999
[Van1998] Van Veldhuizen D.A., Lamont G.B., Evolutionary computation and convergence to a pareto front, Late Breaking Papers at the Genetic Programming 1998 Conference, 221-228, 1998
[Van1999] Van Veldhuizen D.A., Multi-objective evolutionary algorithms: classifications, analysis, and new innovations, PHD Thesis, Department of electrical and computer engineering, air force institute of technology, Wright-Patterson AFB, Ohio, 1999
[Van2000] Van Veldhuizen D.A., Lamont G.B., Multi-objective evolutionary algorithms: Analyzing the state-of-the-art, Evolutionary Computation Journal, vol.8, no.2, 125–148, 2000
[Van2003] Van Veldhuizen D.A., Zydallis J.B., Lamont G.B., Considerations in engineering parallel multi-objective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.7, no.2, 144-173, 2003
[Van2007] Van Dam E., Husslage B., den Hertog D., Melissen H., Maximin Latin hypercube designs in two dimensions, Operations Research, Vol.55, no.1, 158–169, 2007
[Velzer1976] Velzl C., A general theory of the aberrations of diffraction gratings and grating like optical instrument, Journal of Optical Society of America, vol.66, no.4, 346-353, 1976
[Velzer1977] Velzl C., On the imaging properties of holographic gratings, Journal of Optical Society of America, vol.67, no.8, 1021-1027, 1977
[Viana2010] Viana F.A.C., Venter G., Balabanov V., An algorithm for fast optimal Latin hypercube design of experiments, International Journal for Numerical Methods in Engineering, vol.83, no.2, 135-156, 2010
[Vining2005] Vining G.G., Kowalski S.M., Montgomery D.C., Response surface designs within a split-plot structure, Journal of Quality Technology, vol.37, no.2, 115-129, 2005
[Walter2006] Walter I., Hirsch H., Jahn H., Knollenberg J., Venus H., MERTIS– a highly integrated IR imaging spectrometer, Proc. SPIE, 6297, 62970X, 2006
[Wang2007] Wang G.G., Shan S., Review of Metamodeling Techniques in Support of Engineering Design Optimization, ASME J. Mech. Des., Vol.129, no.4, 370–380, 2007
[Werner1967] Werner W., The geometrical optical aberration theory of diffraction gratings, Applied Optics, vol.6, no.10, 1691-1699, 1967
[Wilson2001] Wilson B., Cappelleri D., Frecker M., Simpson T.W., Efficient Pareto Frontier Exploration Using Surrogate Approximations, Optim. Eng., Vol.2, 31-50, 2001
[Ye2000] Ye K.Q., Li W., Sudjianto A., Algorithmic construction of optimal symmetric Latin hypercube designs, Journal of Statistical Planning and Inference, Vol.90, 145–159, 2000
[Yen2003] Yen G.G., Lu H., Dynamic Multi-objective Evolutionary Algorithm: Adaptive Cell-Based Rank and Density Estimation, IEEE transactions on Evolutionary computation, vol.7, no.3, 253-274, 2003
[Zadeh1963] Zadeh L.A., Optimality and nonscalar-valued performance criteria, IEEE Transactions on Automatic Control, vol.8, no.1, 59-60, 1963
[Zhang2007] Zhang Q.F., Li H., MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation, vol.11, no.6, 712?731, 2007
[Zhang2010] Zhang, Q., Liu W., Tsang E., Virginas B., Expensive multi-objective optimization by MOEA/D with Gaussian process model, IEEE Transactions on Evolutionary Computation, vol.14, no.3, 456-474, 2010
[Zitzler1999] Zitzler E., Thiele L., Multi-Objective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Trans. on Evolutionary Computation, vol.3, no.4, 257?271, 1999
[Zitzler2000] Zitzler E., Deb K., Thiele L., Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation Journal, vol.8, no. 2, 173–195, 2000
[Zitzler2002] Zitzler E., Laumanns M., Thiele L., SPEA2: Improving the strength Pareto evolutionary algorithm. In: Giannakoglou K., Tsahalis D.T., Périaux J., Papailiou K.D., Fogarty T., eds. Evolutionary Methods for Design, Optimization and Control with Applications to.Industrial Problems. Berlin: Springer-Verlag, 95?100, 2002
[Zhou2009] Zhou A., Zhang Q., Jin Y., Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation,vol.13, no.5, 1167-1189, 2009
[ChenYR2009]陈一仁,非致冷探测器空间应用的开发,光机电信息,Vol.26, no. 9, 1-6, 2009
[ChenQ2002]陈锵,变间距光栅刻线密度测量,中国科学技术大学硕士学位论文,2002
[CuiXX2001]崔逊学,李淼,方廷健,多目标协调进化算法研究,计算机学报,Vol.24, no.9, 1-6, 2001
[CuiXX2006]崔逊学,多目标进化算法及其应用,国防工业出版社,北京,2006
[DaiGM2009]戴光明,王茂才,多目标优化算法及其在卫星星座设计中的应用,中国地质大学出版社,武汉,2009
[GaoYH2009]高月华,基于Kriging代理模型的优化设计方法及其在注塑成形中的应用,大连理工大学博士毕业论文,2009
[GongGB2009]宫广彪,超光谱成像仪前置成像物镜设计,苏州大学硕士学位论文,2009
[GanFP2002]甘甫平,王润生,杨苏明,西藏Hyperion数据蚀变矿物识别初步研究,国土资源遥感,no.4,52-67, 2002
[HuZW2005]胡中文,广义光栅方程与光栅线密度测试及二维CCD全谱光谱仪的研制,中国科学技术大学博士学位论文,2005
[HuangN2006]黄楠,基于Hyperion数据的地物分类方法研究,东北林业大学硕士毕业论文,2006
[HuangYSH2005a]黄元申,倪争技,同心三反射镜光学系统研究,光学仪器,Vol.27, no.2, 42-46, 2005
[HuangYSH2005b]黄元申,倪争技,庄松林,光栅成像光谱仪同心光学系统研究,光学仪器,Vol.27, no.6, 38-42, 2005
[HuangYSH2007]黄元申,陈家璧,凸面光栅Offner结构成像光谱仪的傅里叶分析,光学仪器,Vol.29, no.6, 40-43, 2007
[HuangYSH2008]黄元申,陈南曙,张大伟,倪争技,庄松林,小型Offner光谱成像系统的设计,光学精密工程,Vol.17, no.11, 2629-2637, 2008
[LeiDM2009]雷德明,多目标智能优化算法及其应用,科学出版社,北京,2009
[TongQX2006]童庆禧,张兵,郑兰芬,高光谱遥感:原理、技术与应用,高等教育出版社,2006
[TongYJ2010a]佟亚军,吴刚,周全等,Offner成像光谱仪的设计方法,光学学报,Vol.30, no.4, 1148-1152, 2010
[TongYJ2010b]佟亚军,软X射线光栅单色器精密测试及成像光谱仪研究,中国科学院研究生院博士学位论文,2010
[WangJ2008]王娟,吕丽军,光栅波像差理论的推广,上海大学学报, Vol.14, no.2, 177-182, 2008
[YanFL2006]阎福礼,王世新,周艺,肖青,祝令亚,王丽涛,焦云清,利用Hyperion星载高光谱传感器监测太湖水质的研究,红外与毫米波学报, Vol.25, no.6, 460-464, 2006
[ZhangCHM2000]张淳民,相里斌,赵葆常,刘良云,干涉成像光谱仪技术的新发展,光学技术,Vol.26, no. 3, 232-234, 2000
[ZhaoBCH2009]赵葆常,杨建峰,常凌颖,陈立武,贺应红,薛彬,嫦娥一号卫星成像光谱仪光学系统设计与在轨评估,光子学报,Vol.38, no.3, 479-483, 2009
[ZHengJH2006]郑金华,多目标进化算法及其应用,科学出版社,北京,2006
[ZHengYQ2005]郑玉权,小型Offner光谱成像系统的设计光学精密工程,Vol.13, no.6, 650-657, 2005
[ZHouQ2010]周全,Offner田野成像光谱仪研制与图像处理,中国科学技术大学博士学位论文,2010