求解规划、聚类和调度问题的混合粒子群算法研究
|
摘要
近来,人们发现专注于单独使用一种算法具有非常大的局限性,如果将元启发式算法与其他优化技术或元启发式算法之间有效结合,即混合元启发式算法,能够更加有效、更加灵活的处理实际问题。本文将粒子群算法与其他技术相结合提出了几种求解规划、聚类和调度问题的混合粒子群算法,并将这些算法与已知的算法进行了比较分析。针对单目标规划问题,将粒子群算法与微分进化算法相结合,提出了一种求解单目标非线性规划问题混合算法DE-PSO;针对多目标规划问题,将传统求解多目标规划问题的分解技术结合到粒子群算中,提出了MCPSO算法。针对聚类问题,将经典的K均值算法引入到粒子群算法中,提出了求解分割聚类问题PKPSO算法和求解动态聚类问题的DKPSO算法。针对流水车间作业调度问题,通过将局部搜索策略、遗传操作及退火策略结合到粒子群算法中,提出了求解单目标流水车间调度问题的SADPSO算法、HPGA算法、AHPSO算法及ATPPSO算法;针对多目标流水车间作业调度问题,将传统求解多目标规划问题的分解技术结合到粒子群算中,提出了MDPSO算法。混合元启发式算法是一个非常新的研究领域,目前对此领域的研究还尚处于初级阶段,本文的研究工作能够对该领域的研究起到促进作用。此外,还为求解这些实际问题提供了几种新的手段。因此,具有一定理论意义和实际价值。
The programming, clustering and scheduling problems are a kind of important optimization problems in our real life which are all NP-hard and difficult to find a satisfying solution within a limited time. How to get the best or a satisfying solution quickly has great significance for improving productivity and the development of soceity. The traditional optimization algorithms have many advantages such as good computing efficiency, strong soundness and mature property, but they all have insurmountable limitation that it is difficult or impossible to find the exactly or even approximately best solution for a complex system. The presentation of evolutionary algorithm gives a new idea for solving complex optimization problems. It has been adopted by many research fields because of its intelligence, universality, stability, essential parallelism and global search ability. Many researchers in our country have put forward lots of optimization algorithms in which the particle swarm optimization algorithm is newer and preferable. It has been applied to many project practice problems and can get very good optimization effects. For many years the main focus of research was on the application of single metaheuristics to given problems. In recent years, it has become evident that the concentration on a sole metaheuristic is rather restrictive. A skilled combination of concepts of different metaheuristics, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility when dealing with real-world and large-scale problems.
In this work, we mainly research the hybrid metaheuristic algorithms based on particle swarm optimization algorithm for the programming, clustering and scheduling problems. That is solvting these problems by combining particle swarm optimization algorithm with other techniques.
For the programming problems, a novel hybrid algorithm DE-PSO is proposed which combines the particle swarm optimization (PSO) algorithm with differential evolution (DE) operators to solve the programming problems with single objective. It incorporates concepts from DE and PSO, updating particle not only by DE operators but also by mechanisms of PSO. A random moving strategy is proposed to enhance the algorithm’s exploration ability and modified DE operators are used to enhance each particle’s local turning ability. These can not only maintain the algorithm’s exploratory feature and at the same time can expedite its convergence. The proposed DE-PSO algorithm is tested on several benchmark functions from the usual literature. Numerical comparisons with different hybrid meta-heuristics demonstrate the effectiveness and efficiencies of the proposed DE-PSO method. In order to find multiple Pareto-optimal solutions, the traditional decomposition methods, stochastic population based algorithms and normal constraint methods for multiobjective programming problems need to run at least one time for each subproblem and each running processing is completely independent, which causes the problem of unable to share information and consuming more time. The obtained solution may also not be comparable. Furthermore, in many real-life applications of multiobjective optimization, may have many or even infinite Pareto optimal vectors. It is very time-consuming, if not impossible, to obtain the complete PF. On the other hand, the decision maker may not be interested in having an unduly large number of Pareto optimal vectors to deal with due to overflow of information. Therefore, many multiobjective optimization algorithms are to find a manageable number of Pareto optimal vectors which are evenly distributed along the PF, and thus good representatives of the entire PF. In this work, we combined the particle swarm optimization algorithm with decomposition methods effectively, and developed the MCPSO algorithm to solve the multiobjective programming problems. In this algorithm, a decomostion method is used to decompose the multiobjective programming problem into a number of scalar optimization subproblems and the particle swarm optimization algorithm is used to optimize them simultaneously. Each subproblem is presented only by one particle and optimized by only using information from its several neighboring subproblems which can not make the candidates share information effectively, but make the algorithm have lower computational complexity. The performance of the algorithm is compared with two multi-objective genetic search algorithms proposed in the literature. Computational results show that the proposed algorithm yields a reasonable approximation of the Pareto optimal set and outperforms NSGA and SPEA significantly for the applied benchmark programming instances.
For the clustering problems, we converted them into optimization problems and integrated the particle swarm optimization algorithm with K-means to solve them. For the partitional clustering problem, an algorithm called PKPSO was proposed, in which the PSO and K-means algorithm are combined together through a new way. By analyzing the algorithm, how to adjust the control parameters is proposed. In order to further improve its performance, the inertia weight is made to change dynamically with the iterations. The comparisons are made between the PKPSO, PSO and K-means algorithm. Through theory analysis and experiments, the PKPSO property of global convergence is proved. It can not only overcome the shortcoming of K-means’s trapping local minimum, but also the solutions precision and the algorithm stability are better than the other two algorithms. However, in partitional clustering algorithms, not only the number of clusters must be specified first but the clustering results are influenced greatly by the initial conditions. A dynamic clustering algorithm called DKPSO is then proposed and the cluster number can be determined automatically during running. In order to relieve the impact of initial conditions, the data set is partitioned into large number clusters, and then the discrete PSO is used to further optimize the cluster number and use the K-means algorithm to optimize the cluster center. Finally, the algorithm validity is testified through experimentation.
For the flowshop scheduling problems, we combined the local search strategies, generic operations and annealing strategies with the particle swarm optimization algorithm and proposed the SADPSO, HPGA, AHPSO and ATPPSO algorithm for the flowshop scheduling problem with single objective. Based on the analysis for each algorithm, their effectivenesses are validated by the comparisons with other existing algorithms on benchmarks with different scale and difficulties. Moreover, an algorithm called MDPSO is developed to solve the multiobjective flowshop scheduling problem, which integrated the particle swarm optimization algorithm with the traditional decomposing methods for multiobjective programming problems. In this way, the multiobjective optimization problem is decomposed into a number of scalar optimization subproblems which can be optimized simultaneously through particle swarm optimization algorithm. Each subproblem is optimized by only using information from its several neighboring subproblems which can not make the candidates share information effectively, but make the algorithm have lower computational complexity. The performance of the algorithm is compared with two multi-objective genetic local search algorithms proposed in the literature. Computational results show that the proposed algorithm yields a reasonable approximation of the Pareto optimal set and outperforms NSGA-II and SPEA-II significantly for the applied benchmark flowshop scheduling instances.
Hybrid metaheuristics is a new research area and currently enjoys an increasing interest in the optimization community since the special issue“Hybrid Metaheuristics”of International Journal of Mathematical Modelling and Algorithms was published in 2005. The design and implementation of hybrid metaheuristics rise problems going beyond questions about the design of a single metaheuristic. Choice and tuning of parameters is for example enlarged by the problem of how to achieve a proper interaction of different algorithm components. Interaction can take place at low-level, using functions from different metaheuristics, but also at high-level, e.g., using a portfolio of metaheuristics for automated hybridization. However, the field of hybrid metaheuristics is still in its early days. Many approaches are pre-mature and a substantial amount of further research is necessary in order to develop clearly structured hybrid metaheuristics that can be generally used for optimization. So, this paper’s work will enrich and push forward the studies of the related areas in both theoretical and technological aspects.
The programming, clustering and scheduling problems are a kind of important optimization problems in our real life which are all NP-hard and difficult to find a satisfying solution within a limited time. How to get the best or a satisfying solution quickly has great significance for improving productivity and the development of soceity. The traditional optimization algorithms have many advantages such as good computing efficiency, strong soundness and mature property, but they all have insurmountable limitation that it is difficult or impossible to find the exactly or even approximately best solution for a complex system. The presentation of evolutionary algorithm gives a new idea for solving complex optimization problems. It has been adopted by many research fields because of its intelligence, universality, stability, essential parallelism and global search ability. Many researchers in our country have put forward lots of optimization algorithms in which the particle swarm optimization algorithm is newer and preferable. It has been applied to many project practice problems and can get very good optimization effects. For many years the main focus of research was on the application of single metaheuristics to given problems. In recent years, it has become evident that the concentration on a sole metaheuristic is rather restrictive. A skilled combination of concepts of different metaheuristics, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility when dealing with real-world and large-scale problems.
In this work, we mainly research the hybrid metaheuristic algorithms based on particle swarm optimization algorithm for the programming, clustering and scheduling problems. That is solvting these problems by combining particle swarm optimization algorithm with other techniques.
For the programming problems, a novel hybrid algorithm DE-PSO is proposed which combines the particle swarm optimization (PSO) algorithm with differential evolution (DE) operators to solve the programming problems with single objective. It incorporates concepts from DE and PSO, updating particle not only by DE operators but also by mechanisms of PSO. A random moving strategy is proposed to enhance the algorithm’s exploration ability and modified DE operators are used to enhance each particle’s local turning ability. These can not only maintain the algorithm’s exploratory feature and at the same time can expedite its convergence. The proposed DE-PSO algorithm is tested on several benchmark functions from the usual literature. Numerical comparisons with different hybrid meta-heuristics demonstrate the effectiveness and efficiencies of the proposed DE-PSO method. In order to find multiple Pareto-optimal solutions, the traditional decomposition methods, stochastic population based algorithms and normal constraint methods for multiobjective programming problems need to run at least one time for each subproblem and each running processing is completely independent, which causes the problem of unable to share information and consuming more time. The obtained solution may also not be comparable. Furthermore, in many real-life applications of multiobjective optimization, may have many or even infinite Pareto optimal vectors. It is very time-consuming, if not impossible, to obtain the complete PF. On the other hand, the decision maker may not be interested in having an unduly large number of Pareto optimal vectors to deal with due to overflow of information. Therefore, many multiobjective optimization algorithms are to find a manageable number of Pareto optimal vectors which are evenly distributed along the PF, and thus good representatives of the entire PF. In this work, we combined the particle swarm optimization algorithm with decomposition methods effectively, and developed the MCPSO algorithm to solve the multiobjective programming problems. In this algorithm, a decomostion method is used to decompose the multiobjective programming problem into a number of scalar optimization subproblems and the particle swarm optimization algorithm is used to optimize them simultaneously. Each subproblem is presented only by one particle and optimized by only using information from its several neighboring subproblems which can not make the candidates share information effectively, but make the algorithm have lower computational complexity. The performance of the algorithm is compared with two multi-objective genetic search algorithms proposed in the literature. Computational results show that the proposed algorithm yields a reasonable approximation of the Pareto optimal set and outperforms NSGA and SPEA significantly for the applied benchmark programming instances.
For the clustering problems, we converted them into optimization problems and integrated the particle swarm optimization algorithm with K-means to solve them. For the partitional clustering problem, an algorithm called PKPSO was proposed, in which the PSO and K-means algorithm are combined together through a new way. By analyzing the algorithm, how to adjust the control parameters is proposed. In order to further improve its performance, the inertia weight is made to change dynamically with the iterations. The comparisons are made between the PKPSO, PSO and K-means algorithm. Through theory analysis and experiments, the PKPSO property of global convergence is proved. It can not only overcome the shortcoming of K-means’s trapping local minimum, but also the solutions precision and the algorithm stability are better than the other two algorithms. However, in partitional clustering algorithms, not only the number of clusters must be specified first but the clustering results are influenced greatly by the initial conditions. A dynamic clustering algorithm called DKPSO is then proposed and the cluster number can be determined automatically during running. In order to relieve the impact of initial conditions, the data set is partitioned into large number clusters, and then the discrete PSO is used to further optimize the cluster number and use the K-means algorithm to optimize the cluster center. Finally, the algorithm validity is testified through experimentation.
For the flowshop scheduling problems, we combined the local search strategies, generic operations and annealing strategies with the particle swarm optimization algorithm and proposed the SADPSO, HPGA, AHPSO and ATPPSO algorithm for the flowshop scheduling problem with single objective. Based on the analysis for each algorithm, their effectivenesses are validated by the comparisons with other existing algorithms on benchmarks with different scale and difficulties. Moreover, an algorithm called MDPSO is developed to solve the multiobjective flowshop scheduling problem, which integrated the particle swarm optimization algorithm with the traditional decomposing methods for multiobjective programming problems. In this way, the multiobjective optimization problem is decomposed into a number of scalar optimization subproblems which can be optimized simultaneously through particle swarm optimization algorithm. Each subproblem is optimized by only using information from its several neighboring subproblems which can not make the candidates share information effectively, but make the algorithm have lower computational complexity. The performance of the algorithm is compared with two multi-objective genetic local search algorithms proposed in the literature. Computational results show that the proposed algorithm yields a reasonable approximation of the Pareto optimal set and outperforms NSGA-II and SPEA-II significantly for the applied benchmark flowshop scheduling instances.
Hybrid metaheuristics is a new research area and currently enjoys an increasing interest in the optimization community since the special issue“Hybrid Metaheuristics”of International Journal of Mathematical Modelling and Algorithms was published in 2005. The design and implementation of hybrid metaheuristics rise problems going beyond questions about the design of a single metaheuristic. Choice and tuning of parameters is for example enlarged by the problem of how to achieve a proper interaction of different algorithm components. Interaction can take place at low-level, using functions from different metaheuristics, but also at high-level, e.g., using a portfolio of metaheuristics for automated hybridization. However, the field of hybrid metaheuristics is still in its early days. Many approaches are pre-mature and a substantial amount of further research is necessary in order to develop clearly structured hybrid metaheuristics that can be generally used for optimization. So, this paper’s work will enrich and push forward the studies of the related areas in both theoretical and technological aspects.
引文
[1].汪定伟,王俊伟,王洪峰等.智能优化方法[M].北京:高等教育出版社, 2007.
[2]. H. Thomas Cormen, E. Charles Leiserson, L.RonaldRivest, and Clifford Stein. Introduction to Algorithms [M], Second Edition. MIT Press and McGraw-Hill, 2001.
[3].徐光辉,刘彦佩,程侃等.运筹学基础手册[M].北京:科学出版社,1999.
[4]. J. M. Buchanan and W. C. Stubblebine. Pareto-Optimality and Gains-from-Trade: A Comment [J]. Economica, 1972, 39(154):203-204.
[5]. A .K. Jain, M. N. Murty, P. J. Flynn. Data Clustering: A Review [J]. ACM Computing Surveys, 1999, 31(3):264-323.
[6]. J.K. LENSTRA, R.K. AHG and P. BRUCKER. Complexity of machine scheduling problems [J]. Annals of Discrete Mathematics, 1977, 1:343-362.
[7]. C. James, Bezdek. On the relationship between neural networks, pattern recognition and intelligence [J]. Int. J. Approx. Reasoning, 1992, 6(2):85-107.
[8]. M. Kevin Passino and Stephen Yurkovich, Fuzzy Control [M], Addison Wesley Longman, Menlo Park, CA, 1998.
[9]. R. Bharath and J. Drosen. Neural Network Computing [M]. McGraw-Hill, 1994.
[10]. Daniel Ashlock, Evolutionary Computation for modeling and Optimization, Publisher: Springer, 2005.
[11]. Eric Bonabeau, Marco Dorigo and Guy Theraulaz. Swarm Intelligence: From Natural to Artificial Systems[M],Oxford University Press, USA; 1 edition,1999.
[12]. M. Zak, Physical model of immune inspired computing [J], Information Sciences, 2000, 129(4):61–79.
[13]. C.C. Maley, DNA computation: Theory, practice, and prospects [J], Evolutionary Computation, 1998(6):201–229.
[14]. C. G. Langton. Studying Artificial Life with Cellular Automata [J]. Physica D, 1986, 10:120-149.
[15].王正志,薄淘.进化计算[M].长沙:国防科技大学出版社, 2000.
[16]. J. H Holland. Adaptationin Natural and Artificial Systems [M].MIT Press, 1975.
[17]. R. Storn, K.Price. Differential evolution–a simple and efficient adaptive scheme for global optimization over continuous spaces[R/OL], 2009-01[2009-04-06]. ICSI, Technical Report TR-95-012, 1995. ftp://ftp.icsi.berkeley.edu/pub/ techreports/ 1995/tr-95-012.pdf.
[18]. K.Uday, Chakraborty, Advances in Differential Evolution [M]. Publisher: Springer, 2008.
[19]. M.M. Millonas. Swarms, Phase Transitions, and Collective Intelligence [M]. IEEE Press, New Jersey, USA, 1994.
[20]. Ying Lin, Jun Zhang, Jing Xiao. A pseudo parallel ant algorithm with an adaptive migration controller [J], Applied Mathematics and Computation, 2008, 205(2):677-687.
[21]. James Kennedy,Eberhart. Swarm Intelligence[M], Publishers:Morgan Kaufmann, 2001.
[22].中国互联网络信息中心.中国互联网络发展状况统计报告[R/OL]. 2009-01[2009-04-06] http://www.micro.caltech.edu/ Courses/EE150/dungeo.
[23]. Kennedy, J.Eberhart.R.C. Particle swarm optimization[C]. In: Proc. of the 1995 IEEE International Conference on Neural Networks. Piscataway, NJ, Perth, Australia: IEEE service center, 1995:1942-1948
[24]. M. Dorigo, V. Maniezzo, A. Colorni, Positive feedback as a search strategy[R]. Technical Report, 1991a:91-016, Italy: University of Milan.
[25]. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: An autocatalytic optimizing process[R]. Technical Report, 1991b:91-016(Revised), Italy: University of Milan.
[26]. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: Optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics–Part B, 1996, 26(1):29–41.
[27]. D. Karaboga, An Idear Based on Honey Bee Swarm for Numerical Optimization[R/OL], 2009-01[2009-04-06], TECHNICAL REPORT-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005. http://mf.erciyes.edu.tr/abc/publ.htm
[28]. Christian Blum, Ant colony optimization: Introduction and recent trends, Physics of Life Reviews, 2(4), (2005): 353-373.
[29].吴斌.群体智能的研究及其在知识发现中的应用[M].中国科学院研究生院博士学位论文, 2002.
[30]. Wu and Z Shi. A Clustering Algorithm Based on Swarm Intelligence[C]. IEEE International Conferences on Info-tech&Info-net Proceeding, Beijing, 58-66, 2001.
[31].张纪会,许心和.具有变异特征的蚁群算法[J].计算机研究与发展, 1999, 36(10):1240-1245.
[32].庄昌文,范明钰,李春辉,虞厥邦.采用面向Agent技术的并行布线系统[J].计算机研究与发展, 1999.36(12).1142-1147
[33].张素兵,石国英,刘泽民,周正.基于蚂蚁算法的QoS路由调度算法[J].电路与系统学报. 2000, 5(1):1-5.
[34].周登勇,戴汝为.一个基于多主体的仿真工具系统COMPLEXITY[J].模式识别与人工智能, 2000, 13(3):241-248.
[35]. Pradyumn Kumar Shukla, Kalyanmoy Deb. On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods [J], 2007, 181(3):1630-1652.
[36]. S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing [J], Science, 1983, 220:671-680.
[37]. S. Forrest, Stephanie. Genetic algorithms: principles of natural selection applied to computation [J]. Science, 1993, 261:872-878.
[38]. N. Mladenovic. A variable neighbourhood algorithm–a new metaheuristic for combinatorial optimization[P]. Abstract of Papers presented at Optimization Days 1995, 112.
[39]. N. Mladenovic, P. Hansen. 1997. Variable Neighborhood Search [J]. Computers & Operations Research, 1997, 24: 1097–1100.
[40]. E. Taillard, Robust taboo search for the quadratic assignment problem [J]. Parallel Computing, 1991, 17:443–455.
[41]. Li-ning Xing, Ying-wu Chen and Huai-ping Cai, An intelligent genetic algorithm designed for global optimization of multi-minima functions [J]. Applied Mathematics and Computation, 2006,178 (2):355-371.
[42]. R. Chelouah, P. Siarry, A continuous genetic algorithm designed for the global optimization of multimodal functions [J]. Journal of Heuristics, 2000 6:191–213.
[43]. R. Chelouah, P. Siarry. Enhanced Continuous Tabu Search: An Algorithm for the Global Optimization ofMultiminima function [J]. Meta-Heuristics, Advances and Trends in Local Search Paradigms for Optimization, 1999:49–61.
[44]. P. Siarry, G. Berthiau, F. Durbin, et al., Enhanced simulated annealing for globally minimizing functions of many continuous variables [J]. ACM Transactions on Mathematical Software, 1997, 23(2):209–228.
[45]. P. Kaelo, M.M. Ali, A numerical study of some modified differential evolution algorithm [J]. European Journal of Operations Research, 2006, 169(3):1176–1184.
[46]. K. Deb, A. Pratap and S. Agarwal, A fast and elitist multiobjective genetic algorithms NSG-II[J]. IEEE Transaction on Evolutionary Computation,2002, 6:82–197.
[47]. C.A.C. Coello, G.T. Pulido and M.S. Lechuga, Handling multiple objectives with particle swarm optimization[J], IEEE Transactions on Evolutionary Computation, 2004, 8:256–279.
[48]. Ajith Abraham, Lakhmi C. Jain, Robert Goldberg. Evolutionary Multiobjective Optimization: Theoretical Advances and Applications [M].Publisher: Springer, 2005.
[49]. L.N. De Castro and J.I. Timmis, Artificial immune systems as a novel soft computing paradigm[J]. Soft Computing, 2003, 7:526–544.
[50]. Mahamed G.H. Omran, Andries P. Engelbrecht, Ayed Salman. Bare bones differential evolution[J]. European Journal of Operational Research, 2009,1(1):128-139.
[51]. D. Karaboga, B. Basturk, On The Performance Of Artificial Bee Colony (ABC) Algorithm[J], Applied Soft Computing,2008, 8(1):687-697.
[52]. Oscar Montiel, Oscar Castillo, Patricia Melin etal., Human evolutionary model: A new approach to optimization[J]. Information Sciences, 2007, 177(10):2075-2098.
[53]. Y.-T. Kao, E. Zahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions[J]. Applied Soft Computing, 2008, 8(2):849-857
[54]. Shu-Kai S. Fan and Erwie Zahara, A hybrid simplex search and particle swarm optimization for unconstrained optimization[J]. European Journal of Operational Research, 2007,181: 527-548.
[55]. S.K. Fan, Y.C. Liang, E. Zahara, Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions[J]. Eng. Optim., 2004, 36:401–418.
[56]. Yong-Jun Wang, Jiang-She Zhang, Gai-Ying Zhang. A dynamic clustering based differential evolution algorithm for global optimization [J]. European Journal of Operational Research, 2007, 183:56–73
[57]. A.K.Jain, R.C. Dubes, Algorithms for Clustering Data. New Jersey: Prentice-Hall Advanced Reference Series, 1988.
[58]. A.K. Jain, P.W. Duin Robert, J.C. Mao, Statistical pattern recognition: A review[J]. IEEE Trans. Actions on Pattern Analysis and Machine Intelligence, 2000,22(1):4-37.
[59]. S. Sambasivam, N. Theodosopoulos, Advanced data clustering methods of mining web documents[J]. Issues in Informing Science and Information Technology, 2006,(3):563-579.
[60].孙吉贵,刘杰等.聚类算法研究[J],软件学报, 2008, 19(1):48-61.
[61]. J.P. Marques Written, Y.F. Wu Trans, Pattern Recognition Concepts, Methods and Applications[M]. 2nd ed., Beijing: Tsinghua University Press, 2002.
[62]. A.L.N. Fred, J.M.N. Leit?o, Partitional vs hierarchical clustering using a minimum grammar complexity approach[C]. In: Proc. of the SSPR&SPR 2000. LNCS 1876, 2000. 193-202.
[63]. R. Gelbard, O. Goldman, I. Spiegler, Investigating diversity of clustering methods: An empirical comparison[J]. Data & Knowledge Engineering, 2007,63(1):155-166.
[64]. P. Kumar, P.R. Krishna, R.S. Bapi, S.K. De, Rough clustering of sequential data[J]. Data & Knowledge Engineering, 2007,3(2):183-199.
[65]. Z. Huang, Extensions to the k-means algorithm for clustering large data sets with categorical values[J]. Data Mining and Knowledge, Discovery II, 1998,(2):283-304.
[66]. A.D. Chaturvedi, P.E. Green, J.D. Carroll, K-modes clustering[J]. Journal of Classification, 2001,18(1):35-56.
[67]. C. Ding, X. He, K-Nearest-Neighbor in data clustering: Incorporating local information into global optimization[J]. In: Proc. of the ACM Symp. on Applied Computing. Nicosia: ACM Press, 2004, 584-589.
[68]. M.S. Yang, Y.J. Hu, etal., Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithm[J]. Journal of Magnetic Resonance Imaging, 2002, 20(2):173-179.
[69]. J. Li, X.B. Gao, L.C. Jiao, A new feature weighted fuzzy clustering algorithm[J]. Chinese Journal of Electronics, 2006,34(1):412-420
[70]. W.L. Cai etal., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007,40(3):825-833.
[71].雷小峰,谢昆青,林帆,夏征义.一种基于K-Means局部最优性的高效聚类算法[J].软件学报. 2008, 19(7): 1683-1692.
[72]. D. Harel, Y. Koren. Clustering spatial data using random walks[C]. In: Proc. of the 7th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining. New York: ACM Press, 2001, 281-286.
[73]. G. Karypis, E.H. Han, V. Kumar, CHANELEON: A hierarchical clustering algorithm using dynamic modeling[J]. IEEE Computer, 1999, 2(8):68-75.
[74]. V. Estivill-Castro, I. Lee, AUTOCLUST: Automatic clustering via boundary extraction for mining massive point-data sets[C]. In: Abrahart J, Carlisle BH, eds. Proc. of the 5th Int’l Conf. on Geocomputation, 2000, 23-25.
[75]. Y.J. Li, A clustering algorithm based on maximal-distant subtrees[J]. Pattern Recognition, 2007,40(5):1425-1431.
[76]. Y. Sun, Q.M. Zhu, Z.X. Chen, An iterative initial-points refinement algorithm for categorical data clustering[J]. Pattern Recognition Letters, 2002,23(7):875-884.
[77]. Y.C. Zhao YC, J. Song, GDILC: A grid-based density isoline clustering algorithm[C]. In: Proc. of the Internet Conf. on Info-net, 2001, 140-145.
[78]. W.M. Ma, E. Chow, W.S. Tommy, A new shifting grid clustering algorithm[J]. Pattern Recognition, 2004,37(3):503-514.
[79]. A.H. Pilevar, M. Sukumar, GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases[J]. Pattern Recognition Letters, 2005,26(7):999-1010.
[80]. M. Nanni, D. Pedreschi, Time-Focused clustering of trajectories of moving objects[J]. Journal of Intelligent Information Systems, 2006, 27(3):267-289.
[81]. D.Birant, A. Kut, ST-DBSCAN: An algorithm for clustering spatial-temporal data[J]. Data & KnowledgeEngineering, 2007,60(1): 208-221.
[82].杨博,刘大有, Liu Jiming等.复杂网格聚类方法[J],软件学报. 2009, 20(1):54-66.
[83]. J.L. Deneubourg, S. Goss, N. Franks, The Dynamics of Collective Sorting: Robot-Like Ant and Ant-Like Robot[C]. In Proceedings First Conference on Simulation of Adaptive Behavior: From Animals to Animats, Cambridge, 1991, 356-365.
[84]. E. Lumer, B. Faieta. Diversity and adaptation in populations of clustering ants[C].In Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Ani-mals to Animats, Cambridge, 1994:499-508.
[85].吴斌,傅伟鹏,郑毅,刘少辉,史忠植.一种基于群体智能的Web文档聚类算法[J].计算机研究与发展, 2002, 39(11):1429-1435.
[86]. C.F. Tsai, C.W. Tsai, H.C. Wu, T. Yang. ACODF: A novel data clustering approach for data mining in large databases. Journal of Systems and Software, 2004,73(1):133-145.
[87].徐晓华,陈崚.一种自适应的蚂蚁聚类算法[J].软件学报, 2006, 17(9):1884-1889.
[88]. Sandra Paterlinia, Thiemo Krinkb. Differential evolution and particle swarm optimisation in partitional clustering[J]. Computational Statistics&Data Analysis, 2006, 50:1220–1247.
[89]. Yi-Tung Kao, Erwie Zahara, I-Wei Kao. A hybridized approach to data clustering [J]. Expert Systems with Applications, 2008, 34:1754–1762.
[90]. S.M. Johnson, Optimal two-and three-stage production schedules with setup times included[J], Naval Research Logistic Quarterly,1954, 1:61–68.
[91]. Rubén Ruiz, Funda Sivrikaya. Modeling realistic hybrid flexible flow shop scheduling problems [J]. Computers& Operations Research, 2008, 35(4):1151-1175.
[92]. Gur Mosheiov, Daniel Oron. Open-shop batch scheduling with identical jobs [J]. European Journal of Operational Research, 2008, 187(3):1282-1292.
[93]. J.K. LENSTRA, R.K. AHG and P. BRUCKER. Complexity of machine scheduling problems [M]. 1977.
[94]. V. C. S. Wiers, A review of the applicability of OR and AI scheduling techniques in practice. Omega, 1997, 25(2):145-153.
[95]. Faiz Al-Khayyal, Paul M. Griffin, Neale R. Smith, Solution of a large-scale two-stage decision and scheduling problem using decomposition[J]. European Journal of Operational Research, 2001, 132(2):453-465.
[96]. Marcos Singer, Decomposition methods for large job shops[J]. Computers& Operations Research, 2001, 28(3):193-207.
[97]. Lixin Tang, Hua Xuan, Jiyin Liu, A new Lagrangian relaxation algorithm for hybrid flow shop scheduling to minimize total weighted completion time[J]. Computers& Operations Research, 2006, 33(11): 3344-3359.
[98]. YoungWoo Kim Inaba, A. Suzuki, T. Okuma, S. et al. Hierarchical scheduling for large-scale production system based on continuous and timed Petri net model[C]. Proceedings of the 41st SICE Annual Conference.Tokyo, Japan: The Society of Instrument and Control Engineers(SICE),2002, 268-271.
[99]. C. Dimopoulos, M.S. Ali. Zalza, Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and Computations[J]. IEEE Transactions on EvolutionaryComputation, 2000,4(2): 93-113.
[100]. Rubén Ruiz, Concepción Maroto, Javier Alcaraz. Two new robust genetic algorithms for the flowshop scheduling problem[J]. Omega, 2006, 34(5):461-476.
[101]. A.C. Nearchou, The effect of various operators on the genetic search for large scheduling problems[J], Int. J. Product. Economy, 2004, 88:191–203.
[102]. Wand Ling. Shop scheduling with genetic algorit hms[M]. Beijing : Tsinghua University Press, 2003.
[103]. C. Dirk Mattfeld, Christian Bierwirth. An efficient genetic algorithm for job shop scheduling with tardiness objectives[J]. European Journal of Operational Research, 2004, 155(3):616-630.
[104]. Kalyanmoy Deb and Abbadi Raji Reddy, Large scale scheduling of casting sequences using a customized genetic algorithm[C], Artificial Evolution Lecture Notes in Computer Science. Berlin, Germany :Springer, 2004,141-152.
[105]. Jie Gao, Linyan Sun, Mitsuo Gen, A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems [J]. Computers& Operations Research, 2008, 35(9): 2892-2907
[106]. T. Murata, H. Ishibuchi, H. Tanaka, Multi-objective genetic algorithm and its applications to flowshop scheduling[J]. Computers and Industrial Engineering, 1996, (30):957–968.
[107]. H. Ishibuchi, T. Murata. A multi-objective genetic local search algorithm and its application to flowshop scheduling[J]. IEEE Transactions on Systems, Man and Cybernetics–Part C: Applications and Reviews, 1998, (28):392–403.
[108]. G. Celano, S. Fichera, V. Grasso, U. La Commare, G. Perrone, An evolutionary approach to multi-objective scheduling of mixed model assembly lines[J]. Computers & Industrial Engineering, 1999, 37(2):69-73.
[109]. X. Cai, K. N. Li. A genetic algorithm for scheduling staff of mixed skills under multi-criteria[J], European Journal of Operational Research, 2000, 125(2):359-369.
[110]. P.C. Chang, S.H. Chen, K.L. Lin, Two phase subpopulation genetic algorithm for parallel machine scheduling problem. Expert Systems with Applications, 2005, 29(3):705–712.
[111]. J.K. Cochran, S.M. Horng, J.W. Fowler, A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines [J]. Computers and Operations Research, 2003, 30:1087–1102.
[112]. Fariborz Jolai, M. Rabbani, S. Amalnick. Genetic algorithm for bi-criteria single machine scheduling problem of minimizing maximum earliness and number of tardy jobs[J]. Applied Mathematics and Computation, 2007, 194(2):552-560.
[113]. H. Ishibuchi, T. Yoshida, T. Murata, Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 2003, 7(2):204–223.
[114]. JoséElias Claudio Arroyo, Vinícius Amaral Armentano. Genetic local search for multi-objective flowshop scheduling problems [J]. European Journal of Operational Research, 2005,167(3):717-738.
[115]. Kuo-Ching Ying, Ching-Jong Liao, An ant colony system for permutation flow-shop sequencing[J], Computers &Operations Research, 2004, 31(5):791-801.
[116]. Godfrey Onwubolu, Donald Davendra. Scheduling flow shops using differential evolution algorithm[J].European Journal of Operational Research, 2006, 171(2):674-692.
[117]. Bassem Jarboui, Saber Ibrahim, Patrick Siarry, Abdelwaheb Rebai. A combinatorial particle swarm optimisation for solving permutation flowshop problems [J]. Computers & Industrial Engineering, 2008, 54(3):526-538.
[118]. D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 2008, 8(1):687-697.
[119]. B. Qian, et al. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers[J]. Computers and Operations Research, 2009, 36(1):209-233.
[120]. Deming Lei, A Pareto archive particle swarm optimization for multi-objective job shop scheduling[J], Computers& Industrial Engineering, 2008, 54(4):960-971.
[121]. Betul Yagmahan, Mehmet Mutlu Yenisey, Ant colony optimization for multi-objective flow shop scheduling problem[J], Computers&Industrial Engineering, 2008, 54(3):411-420.
[122]. M.Clerc: Discrete Particle Swarm Optimization: A Fuzzy Combinatorial Black Box[OL]. In.http://clerc.maurice.free.fr/pso -/Fuzzy_Discrete_PSO/Fuzzy_DPSO.htm
[123]. M.Clerc: Discrete particle swarm optimization, illustrated by the Traveling Salesman Problem[C]. In: New Optimization Techniques in Engineering. Heidelberg, Germany, 2004, 219-239
[124]. L. Schoofs, B. Naudts: Swarm intelligence on the binary constraint satisfaction problem[C]. In: Proc. of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, USA, 2002, 1444-1449.
[125]. Qingyun Yang, Jigui Sun, Juyang Zhang, Chunjie Wang: A Hybrid Discrete Particle Swarm Algorithm for Open-Shop Problems[C]. Proceedings of the 6th International Conference on Simulated Evolution And Learning (SEAL 2006), Hefei, China, LNCS 4247, 158-165.
[126]. Weijun Xia, Zhiming Wu: An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems[J]. Computers & Industrial Engineering, 2005, 48:409-425.
[127]. Chen Ai-ling, Yang Gen-ke, Wu Zhi-ming: Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem[J]. Journal of Zhejiang University SCIENCE A, 2006,7(4):607—614.
[128]. X.Hu, R.C.Eberhart, and Y.Shi. Swarm intelligence for permutation optimization: a case study on n-queens problem[C]. In Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, Indiana, USA, 2003, 243-246.
[129]. S. Geman, D. Geman, Stochastic relaxation, gibbs distribution and bayesian restoration of images[J]. IEEE Trans Patt Anal Machine Intelligence, 1984, 6(6):721-741.
[130]. H.H. Szu, R.L. Hartley, Nonconvex Optimization by Fast Simulated Annealing[J]. Proceeding of IEEE, 1987, 75(3):1538-1540.
[131]. J.H. Holland, Adaption in Natural and Artificial Systems[M]. Ann Arbor Univ. of Michigan Press,1975.
[132]. J.H. Holland, Adaptionin Naturaland Artificial Systems[M]. Boston, MIT Press, 1992.
[133].周春光,梁艳春.计算智能[M].长春:吉林大学出版社, 2001.
[134]. C.A. Coello, Evolutionary Multi-Objective Optimization: Current State and Future Challenges[C]. Fifth International Conference on Hybrid Intelligent Systems (HIS'05), Rio de Janeiro, 2005.
[135]. D.E. Goldberg, Genetic Algorithms in Search, Optimization, Machine Learning [M]. Boston:Addison-Wesley,1989.
[136].席裕庚,柴天佑,恽为民.遗传算法综述[J].控制理论与应用, 1996,13(6):697-708.
[137]. V.Price Kenneth,M.Storn Rainer, A.LampinenJouni. Differential Evolution:A Practical Approach to Global Optimization[M]. Springer, 2004:135-139.
[138]. Tomislav ?muc, Sensitivity of differential evolution algorithm to value of control parameters [C], In Proceedings of the International Conference on Artificial Intelligence, (2002):1087-1093.
[139]. Tomislav ?muc. Improving convergence properties of the differential evolution algorithm [C]. In Proceedings of MENDEL 2002, 8th International Mendel Conference on Soft Computing, (2002):80–86, 2002
[140]. F. van den Bergh, An Analysis of Particle Swarm Optimizers[M], PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002.
[141]. M. Clerc, J. Kennedy, The particle swarm-explosion, stability and convergence in a multidimensional complex space[J], IEEE Transactions on Evolutionary Computation, 2002 6:58–73.
[142]. F. van den Bergh, A.P. Engelbrecht, A new locally convergent particle swarm optimizer[C], In: IEEE International Conference on Systems, Man and Cybernetics, 2002, 96–101.
[143]. J. Kennedy, Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance[C], in: Proceedings of the IEEE Congress on Evolutionary Computation,(1999):1931–1938.
[144]. M.M. Ali, Population set based global optimization algorithms: Some modifications and numerical studies[J], Computers and Operations Research, 2004, 31(10):1703–1725.
[145]. W.J. Zhang, X.F. Xie, D.C. Bi, Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space[C], Congress on Evolutionary Computation, CEC2004, Oregon, USA, 2004, 2(21): 2307-2311.
[146]. R. Chelouah, P. Siarry, A hybrid method combining continuous tabu search and Nelder–Mead simples algorithms for the global optimization of multiminima functions[J]. European Journal of Operational Research, 2005, 161: 636–654.
[147]. R. Chelouah, P. Siarry, Tabu search applied to global optimization[J], European Journal of Operational Research,2000, 123:256–270.
[148]. R. Chelouah, P. Siarry, Genetic and Nelder–Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions[J]. European Journal of Operational Research, 2003, 148:248–335.
[149]. P.S. Shelokar, Patrick Siarry, V.K. Jayaraman and B.D. Kulkarni, Particle swarm and ant colony algorithms hybridized for improved continuous optimization [J]. Applied Mathematics and Computation, 2007, 188(1): 129-142
[150]. Das and J.E.Dennis, A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multi-criteria optimization problems [J], 1997, 14(1):63-69.
[151]. K. Miettinen, Nonlinear Multi-objective Optimization [M]. Norwell, MA: Kluwer, 1999.
[152]. I. Das, J. Dennis, Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multi-criteria optimization problems[J], SIAM Journal of Optimization 8(3) (1998) 631–657.
[153]. A. Messac, C.A. Mattson, Normal constraint method with guarantee of even representation of complete Pareto frontier[J], AIAA Journal 42(10) (2004) 2101–2111.
[154]. Pradyumn Kumar Shukla, On the Normal Boundary Intersection Method for Generation of Efficient Front[C], Lecture Notes In Computer Science, 2007, 4487:310-317.
[155]. E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results[J]. Evolutionary Computation, 2000, 8(2):173-195.
[156].龙海霞,须文波.基于QPSO的数据聚类[J].计算机应用研究. 2006, (12):40-45.
[157].刘向东,沙丘夫.基于粒子群算法的聚类分析[J].计算机工程. 2006, 32(6):201-203.
[158].刘靖明,韩丽川.基于粒子群的K均值聚类算法[J].系统工程理论与实践, 2005, (6):54-58.
[159]. Y.C. Chiou, L.W. Lan, Theory and methodology genetic clustering algorithms[J]. European J. Oper. Res., 2001, (135):413–427.
[160]. C. Merz, P. Murphy, D. Aha, UCI repository of machine learning databases[OL]. Department of Information and Computer Science, University of California, Irvine, http://www.ics.uci.edu/mlearn /MLRepository.html.1997.
[161]. R.C. Eberhart, Y. Shi. Particle swarm optimization: Developments, applications and resources[C], in: Proceedings of the IEEE Congress on Evolutionary Computation, IEEE Press, Seoul, Korea, 2001.
[162]. R.C. Eberhart, Y. Shi. Comparing inertia weights and constriction factors in particle swarm optimization[C], in: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA, 2000, 84–88.
[163]. Lee, C.-Y. Antonsson, E.K. Dynamic partitional clustering using evolution strategies[C], Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE.2000, 4:2716-2721.
[164]. K.Rameshkumar, R.K. Suresh, and K.M.Mohanasundaram: Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makspan[C]. In: Proc. ICNC 2005, LNCS 3612, (2005):572-581.
[165]. Y. Shi, R.C. Eberhart, Parameter selection in particle swarm optimization[C]. In: Evolutionary Programming VII. Lecture Notes in Computer Science, Berlin: Springer, 1998, 1447:591–600.
[166]. E. Taillard. Benchmarks for basic scheduling problems[J], European Journal of Operational Research,1993, 64:278–285.
[167]. S.G. Ponnambalam, P. Aravindan, S. Chandrasekaran, Constructive and improvement flow shop scheduling heuristics: an extensive evaluation[J], Production Planning & Control, 2001, 12(4):335-344.
[168]. L. Wang, D.Z. Zheng. An Effective Hybrid heuristic for flowshop scheduling [J]. International Journal of Advanced manufacturing technology, 2003, 21:38-44.
[169]. Zhigang Lian, Xingsheng Gu and Bin Jiao, A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan[J]. Applied Mathematics and Computation, 2006, 175(1):773-785.
[170]. Lian Zhigang, Xingsheng Gu and Bin Jiao, A novel particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan[J]. Chaos, Solitons & Fractals, 2008, 35(5): 851-861.
[171]. F. van den Bergh. An Analysis of Particle Swarm Optimizers[OL]. PhD thesis, Department of ComputerScience, University of Pretoria, Pretoria, South Africa, 2002. http://upetd.up.ac.za/thesis/available /etd-05032006-160549/ unrestricted/00thesis.pdf
[172]. Quan-Ke Pan, M. Fatih Tasgetiren and Yun-Chia Liang. A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem[J]. Computers & Operations Research, 2008, 35(9):2807-2839.
[173]. E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm[R]. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland. 2001.
[174]. K. Deb, P. Amrit, A. Sameer, and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182–197.
[175]. E. G. Talbi, M. Rahoual, M. H. Mabed, and C. Dhaenens. A hybrid evolutionary approach for multicriteria optimization problems: Application to the flow shop[J]. Evolutionary Multi-Criterion Optimization, 2001, 1993:416–428.
[2]. H. Thomas Cormen, E. Charles Leiserson, L.RonaldRivest, and Clifford Stein. Introduction to Algorithms [M], Second Edition. MIT Press and McGraw-Hill, 2001.
[3].徐光辉,刘彦佩,程侃等.运筹学基础手册[M].北京:科学出版社,1999.
[4]. J. M. Buchanan and W. C. Stubblebine. Pareto-Optimality and Gains-from-Trade: A Comment [J]. Economica, 1972, 39(154):203-204.
[5]. A .K. Jain, M. N. Murty, P. J. Flynn. Data Clustering: A Review [J]. ACM Computing Surveys, 1999, 31(3):264-323.
[6]. J.K. LENSTRA, R.K. AHG and P. BRUCKER. Complexity of machine scheduling problems [J]. Annals of Discrete Mathematics, 1977, 1:343-362.
[7]. C. James, Bezdek. On the relationship between neural networks, pattern recognition and intelligence [J]. Int. J. Approx. Reasoning, 1992, 6(2):85-107.
[8]. M. Kevin Passino and Stephen Yurkovich, Fuzzy Control [M], Addison Wesley Longman, Menlo Park, CA, 1998.
[9]. R. Bharath and J. Drosen. Neural Network Computing [M]. McGraw-Hill, 1994.
[10]. Daniel Ashlock, Evolutionary Computation for modeling and Optimization, Publisher: Springer, 2005.
[11]. Eric Bonabeau, Marco Dorigo and Guy Theraulaz. Swarm Intelligence: From Natural to Artificial Systems[M],Oxford University Press, USA; 1 edition,1999.
[12]. M. Zak, Physical model of immune inspired computing [J], Information Sciences, 2000, 129(4):61–79.
[13]. C.C. Maley, DNA computation: Theory, practice, and prospects [J], Evolutionary Computation, 1998(6):201–229.
[14]. C. G. Langton. Studying Artificial Life with Cellular Automata [J]. Physica D, 1986, 10:120-149.
[15].王正志,薄淘.进化计算[M].长沙:国防科技大学出版社, 2000.
[16]. J. H Holland. Adaptationin Natural and Artificial Systems [M].MIT Press, 1975.
[17]. R. Storn, K.Price. Differential evolution–a simple and efficient adaptive scheme for global optimization over continuous spaces[R/OL], 2009-01[2009-04-06]. ICSI, Technical Report TR-95-012, 1995. ftp://ftp.icsi.berkeley.edu/pub/ techreports/ 1995/tr-95-012.pdf.
[18]. K.Uday, Chakraborty, Advances in Differential Evolution [M]. Publisher: Springer, 2008.
[19]. M.M. Millonas. Swarms, Phase Transitions, and Collective Intelligence [M]. IEEE Press, New Jersey, USA, 1994.
[20]. Ying Lin, Jun Zhang, Jing Xiao. A pseudo parallel ant algorithm with an adaptive migration controller [J], Applied Mathematics and Computation, 2008, 205(2):677-687.
[21]. James Kennedy,Eberhart. Swarm Intelligence[M], Publishers:Morgan Kaufmann, 2001.
[22].中国互联网络信息中心.中国互联网络发展状况统计报告[R/OL]. 2009-01[2009-04-06] http://www.micro.caltech.edu/ Courses/EE150/dungeo.
[23]. Kennedy, J.Eberhart.R.C. Particle swarm optimization[C]. In: Proc. of the 1995 IEEE International Conference on Neural Networks. Piscataway, NJ, Perth, Australia: IEEE service center, 1995:1942-1948
[24]. M. Dorigo, V. Maniezzo, A. Colorni, Positive feedback as a search strategy[R]. Technical Report, 1991a:91-016, Italy: University of Milan.
[25]. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: An autocatalytic optimizing process[R]. Technical Report, 1991b:91-016(Revised), Italy: University of Milan.
[26]. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: Optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics–Part B, 1996, 26(1):29–41.
[27]. D. Karaboga, An Idear Based on Honey Bee Swarm for Numerical Optimization[R/OL], 2009-01[2009-04-06], TECHNICAL REPORT-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005. http://mf.erciyes.edu.tr/abc/publ.htm
[28]. Christian Blum, Ant colony optimization: Introduction and recent trends, Physics of Life Reviews, 2(4), (2005): 353-373.
[29].吴斌.群体智能的研究及其在知识发现中的应用[M].中国科学院研究生院博士学位论文, 2002.
[30]. Wu and Z Shi. A Clustering Algorithm Based on Swarm Intelligence[C]. IEEE International Conferences on Info-tech&Info-net Proceeding, Beijing, 58-66, 2001.
[31].张纪会,许心和.具有变异特征的蚁群算法[J].计算机研究与发展, 1999, 36(10):1240-1245.
[32].庄昌文,范明钰,李春辉,虞厥邦.采用面向Agent技术的并行布线系统[J].计算机研究与发展, 1999.36(12).1142-1147
[33].张素兵,石国英,刘泽民,周正.基于蚂蚁算法的QoS路由调度算法[J].电路与系统学报. 2000, 5(1):1-5.
[34].周登勇,戴汝为.一个基于多主体的仿真工具系统COMPLEXITY[J].模式识别与人工智能, 2000, 13(3):241-248.
[35]. Pradyumn Kumar Shukla, Kalyanmoy Deb. On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods [J], 2007, 181(3):1630-1652.
[36]. S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing [J], Science, 1983, 220:671-680.
[37]. S. Forrest, Stephanie. Genetic algorithms: principles of natural selection applied to computation [J]. Science, 1993, 261:872-878.
[38]. N. Mladenovic. A variable neighbourhood algorithm–a new metaheuristic for combinatorial optimization[P]. Abstract of Papers presented at Optimization Days 1995, 112.
[39]. N. Mladenovic, P. Hansen. 1997. Variable Neighborhood Search [J]. Computers & Operations Research, 1997, 24: 1097–1100.
[40]. E. Taillard, Robust taboo search for the quadratic assignment problem [J]. Parallel Computing, 1991, 17:443–455.
[41]. Li-ning Xing, Ying-wu Chen and Huai-ping Cai, An intelligent genetic algorithm designed for global optimization of multi-minima functions [J]. Applied Mathematics and Computation, 2006,178 (2):355-371.
[42]. R. Chelouah, P. Siarry, A continuous genetic algorithm designed for the global optimization of multimodal functions [J]. Journal of Heuristics, 2000 6:191–213.
[43]. R. Chelouah, P. Siarry. Enhanced Continuous Tabu Search: An Algorithm for the Global Optimization ofMultiminima function [J]. Meta-Heuristics, Advances and Trends in Local Search Paradigms for Optimization, 1999:49–61.
[44]. P. Siarry, G. Berthiau, F. Durbin, et al., Enhanced simulated annealing for globally minimizing functions of many continuous variables [J]. ACM Transactions on Mathematical Software, 1997, 23(2):209–228.
[45]. P. Kaelo, M.M. Ali, A numerical study of some modified differential evolution algorithm [J]. European Journal of Operations Research, 2006, 169(3):1176–1184.
[46]. K. Deb, A. Pratap and S. Agarwal, A fast and elitist multiobjective genetic algorithms NSG-II[J]. IEEE Transaction on Evolutionary Computation,2002, 6:82–197.
[47]. C.A.C. Coello, G.T. Pulido and M.S. Lechuga, Handling multiple objectives with particle swarm optimization[J], IEEE Transactions on Evolutionary Computation, 2004, 8:256–279.
[48]. Ajith Abraham, Lakhmi C. Jain, Robert Goldberg. Evolutionary Multiobjective Optimization: Theoretical Advances and Applications [M].Publisher: Springer, 2005.
[49]. L.N. De Castro and J.I. Timmis, Artificial immune systems as a novel soft computing paradigm[J]. Soft Computing, 2003, 7:526–544.
[50]. Mahamed G.H. Omran, Andries P. Engelbrecht, Ayed Salman. Bare bones differential evolution[J]. European Journal of Operational Research, 2009,1(1):128-139.
[51]. D. Karaboga, B. Basturk, On The Performance Of Artificial Bee Colony (ABC) Algorithm[J], Applied Soft Computing,2008, 8(1):687-697.
[52]. Oscar Montiel, Oscar Castillo, Patricia Melin etal., Human evolutionary model: A new approach to optimization[J]. Information Sciences, 2007, 177(10):2075-2098.
[53]. Y.-T. Kao, E. Zahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions[J]. Applied Soft Computing, 2008, 8(2):849-857
[54]. Shu-Kai S. Fan and Erwie Zahara, A hybrid simplex search and particle swarm optimization for unconstrained optimization[J]. European Journal of Operational Research, 2007,181: 527-548.
[55]. S.K. Fan, Y.C. Liang, E. Zahara, Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions[J]. Eng. Optim., 2004, 36:401–418.
[56]. Yong-Jun Wang, Jiang-She Zhang, Gai-Ying Zhang. A dynamic clustering based differential evolution algorithm for global optimization [J]. European Journal of Operational Research, 2007, 183:56–73
[57]. A.K.Jain, R.C. Dubes, Algorithms for Clustering Data. New Jersey: Prentice-Hall Advanced Reference Series, 1988.
[58]. A.K. Jain, P.W. Duin Robert, J.C. Mao, Statistical pattern recognition: A review[J]. IEEE Trans. Actions on Pattern Analysis and Machine Intelligence, 2000,22(1):4-37.
[59]. S. Sambasivam, N. Theodosopoulos, Advanced data clustering methods of mining web documents[J]. Issues in Informing Science and Information Technology, 2006,(3):563-579.
[60].孙吉贵,刘杰等.聚类算法研究[J],软件学报, 2008, 19(1):48-61.
[61]. J.P. Marques Written, Y.F. Wu Trans, Pattern Recognition Concepts, Methods and Applications[M]. 2nd ed., Beijing: Tsinghua University Press, 2002.
[62]. A.L.N. Fred, J.M.N. Leit?o, Partitional vs hierarchical clustering using a minimum grammar complexity approach[C]. In: Proc. of the SSPR&SPR 2000. LNCS 1876, 2000. 193-202.
[63]. R. Gelbard, O. Goldman, I. Spiegler, Investigating diversity of clustering methods: An empirical comparison[J]. Data & Knowledge Engineering, 2007,63(1):155-166.
[64]. P. Kumar, P.R. Krishna, R.S. Bapi, S.K. De, Rough clustering of sequential data[J]. Data & Knowledge Engineering, 2007,3(2):183-199.
[65]. Z. Huang, Extensions to the k-means algorithm for clustering large data sets with categorical values[J]. Data Mining and Knowledge, Discovery II, 1998,(2):283-304.
[66]. A.D. Chaturvedi, P.E. Green, J.D. Carroll, K-modes clustering[J]. Journal of Classification, 2001,18(1):35-56.
[67]. C. Ding, X. He, K-Nearest-Neighbor in data clustering: Incorporating local information into global optimization[J]. In: Proc. of the ACM Symp. on Applied Computing. Nicosia: ACM Press, 2004, 584-589.
[68]. M.S. Yang, Y.J. Hu, etal., Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithm[J]. Journal of Magnetic Resonance Imaging, 2002, 20(2):173-179.
[69]. J. Li, X.B. Gao, L.C. Jiao, A new feature weighted fuzzy clustering algorithm[J]. Chinese Journal of Electronics, 2006,34(1):412-420
[70]. W.L. Cai etal., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007,40(3):825-833.
[71].雷小峰,谢昆青,林帆,夏征义.一种基于K-Means局部最优性的高效聚类算法[J].软件学报. 2008, 19(7): 1683-1692.
[72]. D. Harel, Y. Koren. Clustering spatial data using random walks[C]. In: Proc. of the 7th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining. New York: ACM Press, 2001, 281-286.
[73]. G. Karypis, E.H. Han, V. Kumar, CHANELEON: A hierarchical clustering algorithm using dynamic modeling[J]. IEEE Computer, 1999, 2(8):68-75.
[74]. V. Estivill-Castro, I. Lee, AUTOCLUST: Automatic clustering via boundary extraction for mining massive point-data sets[C]. In: Abrahart J, Carlisle BH, eds. Proc. of the 5th Int’l Conf. on Geocomputation, 2000, 23-25.
[75]. Y.J. Li, A clustering algorithm based on maximal-distant subtrees[J]. Pattern Recognition, 2007,40(5):1425-1431.
[76]. Y. Sun, Q.M. Zhu, Z.X. Chen, An iterative initial-points refinement algorithm for categorical data clustering[J]. Pattern Recognition Letters, 2002,23(7):875-884.
[77]. Y.C. Zhao YC, J. Song, GDILC: A grid-based density isoline clustering algorithm[C]. In: Proc. of the Internet Conf. on Info-net, 2001, 140-145.
[78]. W.M. Ma, E. Chow, W.S. Tommy, A new shifting grid clustering algorithm[J]. Pattern Recognition, 2004,37(3):503-514.
[79]. A.H. Pilevar, M. Sukumar, GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases[J]. Pattern Recognition Letters, 2005,26(7):999-1010.
[80]. M. Nanni, D. Pedreschi, Time-Focused clustering of trajectories of moving objects[J]. Journal of Intelligent Information Systems, 2006, 27(3):267-289.
[81]. D.Birant, A. Kut, ST-DBSCAN: An algorithm for clustering spatial-temporal data[J]. Data & KnowledgeEngineering, 2007,60(1): 208-221.
[82].杨博,刘大有, Liu Jiming等.复杂网格聚类方法[J],软件学报. 2009, 20(1):54-66.
[83]. J.L. Deneubourg, S. Goss, N. Franks, The Dynamics of Collective Sorting: Robot-Like Ant and Ant-Like Robot[C]. In Proceedings First Conference on Simulation of Adaptive Behavior: From Animals to Animats, Cambridge, 1991, 356-365.
[84]. E. Lumer, B. Faieta. Diversity and adaptation in populations of clustering ants[C].In Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Ani-mals to Animats, Cambridge, 1994:499-508.
[85].吴斌,傅伟鹏,郑毅,刘少辉,史忠植.一种基于群体智能的Web文档聚类算法[J].计算机研究与发展, 2002, 39(11):1429-1435.
[86]. C.F. Tsai, C.W. Tsai, H.C. Wu, T. Yang. ACODF: A novel data clustering approach for data mining in large databases. Journal of Systems and Software, 2004,73(1):133-145.
[87].徐晓华,陈崚.一种自适应的蚂蚁聚类算法[J].软件学报, 2006, 17(9):1884-1889.
[88]. Sandra Paterlinia, Thiemo Krinkb. Differential evolution and particle swarm optimisation in partitional clustering[J]. Computational Statistics&Data Analysis, 2006, 50:1220–1247.
[89]. Yi-Tung Kao, Erwie Zahara, I-Wei Kao. A hybridized approach to data clustering [J]. Expert Systems with Applications, 2008, 34:1754–1762.
[90]. S.M. Johnson, Optimal two-and three-stage production schedules with setup times included[J], Naval Research Logistic Quarterly,1954, 1:61–68.
[91]. Rubén Ruiz, Funda Sivrikaya. Modeling realistic hybrid flexible flow shop scheduling problems [J]. Computers& Operations Research, 2008, 35(4):1151-1175.
[92]. Gur Mosheiov, Daniel Oron. Open-shop batch scheduling with identical jobs [J]. European Journal of Operational Research, 2008, 187(3):1282-1292.
[93]. J.K. LENSTRA, R.K. AHG and P. BRUCKER. Complexity of machine scheduling problems [M]. 1977.
[94]. V. C. S. Wiers, A review of the applicability of OR and AI scheduling techniques in practice. Omega, 1997, 25(2):145-153.
[95]. Faiz Al-Khayyal, Paul M. Griffin, Neale R. Smith, Solution of a large-scale two-stage decision and scheduling problem using decomposition[J]. European Journal of Operational Research, 2001, 132(2):453-465.
[96]. Marcos Singer, Decomposition methods for large job shops[J]. Computers& Operations Research, 2001, 28(3):193-207.
[97]. Lixin Tang, Hua Xuan, Jiyin Liu, A new Lagrangian relaxation algorithm for hybrid flow shop scheduling to minimize total weighted completion time[J]. Computers& Operations Research, 2006, 33(11): 3344-3359.
[98]. YoungWoo Kim Inaba, A. Suzuki, T. Okuma, S. et al. Hierarchical scheduling for large-scale production system based on continuous and timed Petri net model[C]. Proceedings of the 41st SICE Annual Conference.Tokyo, Japan: The Society of Instrument and Control Engineers(SICE),2002, 268-271.
[99]. C. Dimopoulos, M.S. Ali. Zalza, Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and Computations[J]. IEEE Transactions on EvolutionaryComputation, 2000,4(2): 93-113.
[100]. Rubén Ruiz, Concepción Maroto, Javier Alcaraz. Two new robust genetic algorithms for the flowshop scheduling problem[J]. Omega, 2006, 34(5):461-476.
[101]. A.C. Nearchou, The effect of various operators on the genetic search for large scheduling problems[J], Int. J. Product. Economy, 2004, 88:191–203.
[102]. Wand Ling. Shop scheduling with genetic algorit hms[M]. Beijing : Tsinghua University Press, 2003.
[103]. C. Dirk Mattfeld, Christian Bierwirth. An efficient genetic algorithm for job shop scheduling with tardiness objectives[J]. European Journal of Operational Research, 2004, 155(3):616-630.
[104]. Kalyanmoy Deb and Abbadi Raji Reddy, Large scale scheduling of casting sequences using a customized genetic algorithm[C], Artificial Evolution Lecture Notes in Computer Science. Berlin, Germany :Springer, 2004,141-152.
[105]. Jie Gao, Linyan Sun, Mitsuo Gen, A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems [J]. Computers& Operations Research, 2008, 35(9): 2892-2907
[106]. T. Murata, H. Ishibuchi, H. Tanaka, Multi-objective genetic algorithm and its applications to flowshop scheduling[J]. Computers and Industrial Engineering, 1996, (30):957–968.
[107]. H. Ishibuchi, T. Murata. A multi-objective genetic local search algorithm and its application to flowshop scheduling[J]. IEEE Transactions on Systems, Man and Cybernetics–Part C: Applications and Reviews, 1998, (28):392–403.
[108]. G. Celano, S. Fichera, V. Grasso, U. La Commare, G. Perrone, An evolutionary approach to multi-objective scheduling of mixed model assembly lines[J]. Computers & Industrial Engineering, 1999, 37(2):69-73.
[109]. X. Cai, K. N. Li. A genetic algorithm for scheduling staff of mixed skills under multi-criteria[J], European Journal of Operational Research, 2000, 125(2):359-369.
[110]. P.C. Chang, S.H. Chen, K.L. Lin, Two phase subpopulation genetic algorithm for parallel machine scheduling problem. Expert Systems with Applications, 2005, 29(3):705–712.
[111]. J.K. Cochran, S.M. Horng, J.W. Fowler, A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines [J]. Computers and Operations Research, 2003, 30:1087–1102.
[112]. Fariborz Jolai, M. Rabbani, S. Amalnick. Genetic algorithm for bi-criteria single machine scheduling problem of minimizing maximum earliness and number of tardy jobs[J]. Applied Mathematics and Computation, 2007, 194(2):552-560.
[113]. H. Ishibuchi, T. Yoshida, T. Murata, Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 2003, 7(2):204–223.
[114]. JoséElias Claudio Arroyo, Vinícius Amaral Armentano. Genetic local search for multi-objective flowshop scheduling problems [J]. European Journal of Operational Research, 2005,167(3):717-738.
[115]. Kuo-Ching Ying, Ching-Jong Liao, An ant colony system for permutation flow-shop sequencing[J], Computers &Operations Research, 2004, 31(5):791-801.
[116]. Godfrey Onwubolu, Donald Davendra. Scheduling flow shops using differential evolution algorithm[J].European Journal of Operational Research, 2006, 171(2):674-692.
[117]. Bassem Jarboui, Saber Ibrahim, Patrick Siarry, Abdelwaheb Rebai. A combinatorial particle swarm optimisation for solving permutation flowshop problems [J]. Computers & Industrial Engineering, 2008, 54(3):526-538.
[118]. D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 2008, 8(1):687-697.
[119]. B. Qian, et al. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers[J]. Computers and Operations Research, 2009, 36(1):209-233.
[120]. Deming Lei, A Pareto archive particle swarm optimization for multi-objective job shop scheduling[J], Computers& Industrial Engineering, 2008, 54(4):960-971.
[121]. Betul Yagmahan, Mehmet Mutlu Yenisey, Ant colony optimization for multi-objective flow shop scheduling problem[J], Computers&Industrial Engineering, 2008, 54(3):411-420.
[122]. M.Clerc: Discrete Particle Swarm Optimization: A Fuzzy Combinatorial Black Box[OL]. In.http://clerc.maurice.free.fr/pso -/Fuzzy_Discrete_PSO/Fuzzy_DPSO.htm
[123]. M.Clerc: Discrete particle swarm optimization, illustrated by the Traveling Salesman Problem[C]. In: New Optimization Techniques in Engineering. Heidelberg, Germany, 2004, 219-239
[124]. L. Schoofs, B. Naudts: Swarm intelligence on the binary constraint satisfaction problem[C]. In: Proc. of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, USA, 2002, 1444-1449.
[125]. Qingyun Yang, Jigui Sun, Juyang Zhang, Chunjie Wang: A Hybrid Discrete Particle Swarm Algorithm for Open-Shop Problems[C]. Proceedings of the 6th International Conference on Simulated Evolution And Learning (SEAL 2006), Hefei, China, LNCS 4247, 158-165.
[126]. Weijun Xia, Zhiming Wu: An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems[J]. Computers & Industrial Engineering, 2005, 48:409-425.
[127]. Chen Ai-ling, Yang Gen-ke, Wu Zhi-ming: Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem[J]. Journal of Zhejiang University SCIENCE A, 2006,7(4):607—614.
[128]. X.Hu, R.C.Eberhart, and Y.Shi. Swarm intelligence for permutation optimization: a case study on n-queens problem[C]. In Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, Indiana, USA, 2003, 243-246.
[129]. S. Geman, D. Geman, Stochastic relaxation, gibbs distribution and bayesian restoration of images[J]. IEEE Trans Patt Anal Machine Intelligence, 1984, 6(6):721-741.
[130]. H.H. Szu, R.L. Hartley, Nonconvex Optimization by Fast Simulated Annealing[J]. Proceeding of IEEE, 1987, 75(3):1538-1540.
[131]. J.H. Holland, Adaption in Natural and Artificial Systems[M]. Ann Arbor Univ. of Michigan Press,1975.
[132]. J.H. Holland, Adaptionin Naturaland Artificial Systems[M]. Boston, MIT Press, 1992.
[133].周春光,梁艳春.计算智能[M].长春:吉林大学出版社, 2001.
[134]. C.A. Coello, Evolutionary Multi-Objective Optimization: Current State and Future Challenges[C]. Fifth International Conference on Hybrid Intelligent Systems (HIS'05), Rio de Janeiro, 2005.
[135]. D.E. Goldberg, Genetic Algorithms in Search, Optimization, Machine Learning [M]. Boston:Addison-Wesley,1989.
[136].席裕庚,柴天佑,恽为民.遗传算法综述[J].控制理论与应用, 1996,13(6):697-708.
[137]. V.Price Kenneth,M.Storn Rainer, A.LampinenJouni. Differential Evolution:A Practical Approach to Global Optimization[M]. Springer, 2004:135-139.
[138]. Tomislav ?muc, Sensitivity of differential evolution algorithm to value of control parameters [C], In Proceedings of the International Conference on Artificial Intelligence, (2002):1087-1093.
[139]. Tomislav ?muc. Improving convergence properties of the differential evolution algorithm [C]. In Proceedings of MENDEL 2002, 8th International Mendel Conference on Soft Computing, (2002):80–86, 2002
[140]. F. van den Bergh, An Analysis of Particle Swarm Optimizers[M], PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002.
[141]. M. Clerc, J. Kennedy, The particle swarm-explosion, stability and convergence in a multidimensional complex space[J], IEEE Transactions on Evolutionary Computation, 2002 6:58–73.
[142]. F. van den Bergh, A.P. Engelbrecht, A new locally convergent particle swarm optimizer[C], In: IEEE International Conference on Systems, Man and Cybernetics, 2002, 96–101.
[143]. J. Kennedy, Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance[C], in: Proceedings of the IEEE Congress on Evolutionary Computation,(1999):1931–1938.
[144]. M.M. Ali, Population set based global optimization algorithms: Some modifications and numerical studies[J], Computers and Operations Research, 2004, 31(10):1703–1725.
[145]. W.J. Zhang, X.F. Xie, D.C. Bi, Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space[C], Congress on Evolutionary Computation, CEC2004, Oregon, USA, 2004, 2(21): 2307-2311.
[146]. R. Chelouah, P. Siarry, A hybrid method combining continuous tabu search and Nelder–Mead simples algorithms for the global optimization of multiminima functions[J]. European Journal of Operational Research, 2005, 161: 636–654.
[147]. R. Chelouah, P. Siarry, Tabu search applied to global optimization[J], European Journal of Operational Research,2000, 123:256–270.
[148]. R. Chelouah, P. Siarry, Genetic and Nelder–Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions[J]. European Journal of Operational Research, 2003, 148:248–335.
[149]. P.S. Shelokar, Patrick Siarry, V.K. Jayaraman and B.D. Kulkarni, Particle swarm and ant colony algorithms hybridized for improved continuous optimization [J]. Applied Mathematics and Computation, 2007, 188(1): 129-142
[150]. Das and J.E.Dennis, A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multi-criteria optimization problems [J], 1997, 14(1):63-69.
[151]. K. Miettinen, Nonlinear Multi-objective Optimization [M]. Norwell, MA: Kluwer, 1999.
[152]. I. Das, J. Dennis, Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multi-criteria optimization problems[J], SIAM Journal of Optimization 8(3) (1998) 631–657.
[153]. A. Messac, C.A. Mattson, Normal constraint method with guarantee of even representation of complete Pareto frontier[J], AIAA Journal 42(10) (2004) 2101–2111.
[154]. Pradyumn Kumar Shukla, On the Normal Boundary Intersection Method for Generation of Efficient Front[C], Lecture Notes In Computer Science, 2007, 4487:310-317.
[155]. E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results[J]. Evolutionary Computation, 2000, 8(2):173-195.
[156].龙海霞,须文波.基于QPSO的数据聚类[J].计算机应用研究. 2006, (12):40-45.
[157].刘向东,沙丘夫.基于粒子群算法的聚类分析[J].计算机工程. 2006, 32(6):201-203.
[158].刘靖明,韩丽川.基于粒子群的K均值聚类算法[J].系统工程理论与实践, 2005, (6):54-58.
[159]. Y.C. Chiou, L.W. Lan, Theory and methodology genetic clustering algorithms[J]. European J. Oper. Res., 2001, (135):413–427.
[160]. C. Merz, P. Murphy, D. Aha, UCI repository of machine learning databases[OL]. Department of Information and Computer Science, University of California, Irvine, http://www.ics.uci.edu/mlearn /MLRepository.html.1997.
[161]. R.C. Eberhart, Y. Shi. Particle swarm optimization: Developments, applications and resources[C], in: Proceedings of the IEEE Congress on Evolutionary Computation, IEEE Press, Seoul, Korea, 2001.
[162]. R.C. Eberhart, Y. Shi. Comparing inertia weights and constriction factors in particle swarm optimization[C], in: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA, 2000, 84–88.
[163]. Lee, C.-Y. Antonsson, E.K. Dynamic partitional clustering using evolution strategies[C], Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE.2000, 4:2716-2721.
[164]. K.Rameshkumar, R.K. Suresh, and K.M.Mohanasundaram: Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makspan[C]. In: Proc. ICNC 2005, LNCS 3612, (2005):572-581.
[165]. Y. Shi, R.C. Eberhart, Parameter selection in particle swarm optimization[C]. In: Evolutionary Programming VII. Lecture Notes in Computer Science, Berlin: Springer, 1998, 1447:591–600.
[166]. E. Taillard. Benchmarks for basic scheduling problems[J], European Journal of Operational Research,1993, 64:278–285.
[167]. S.G. Ponnambalam, P. Aravindan, S. Chandrasekaran, Constructive and improvement flow shop scheduling heuristics: an extensive evaluation[J], Production Planning & Control, 2001, 12(4):335-344.
[168]. L. Wang, D.Z. Zheng. An Effective Hybrid heuristic for flowshop scheduling [J]. International Journal of Advanced manufacturing technology, 2003, 21:38-44.
[169]. Zhigang Lian, Xingsheng Gu and Bin Jiao, A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan[J]. Applied Mathematics and Computation, 2006, 175(1):773-785.
[170]. Lian Zhigang, Xingsheng Gu and Bin Jiao, A novel particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan[J]. Chaos, Solitons & Fractals, 2008, 35(5): 851-861.
[171]. F. van den Bergh. An Analysis of Particle Swarm Optimizers[OL]. PhD thesis, Department of ComputerScience, University of Pretoria, Pretoria, South Africa, 2002. http://upetd.up.ac.za/thesis/available /etd-05032006-160549/ unrestricted/00thesis.pdf
[172]. Quan-Ke Pan, M. Fatih Tasgetiren and Yun-Chia Liang. A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem[J]. Computers & Operations Research, 2008, 35(9):2807-2839.
[173]. E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm[R]. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland. 2001.
[174]. K. Deb, P. Amrit, A. Sameer, and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182–197.
[175]. E. G. Talbi, M. Rahoual, M. H. Mabed, and C. Dhaenens. A hybrid evolutionary approach for multicriteria optimization problems: Application to the flow shop[J]. Evolutionary Multi-Criterion Optimization, 2001, 1993:416–428.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |