基于人工免疫系统的多目标优化与SAR图像分割
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摘要
最优化问题是工程实践和科学研究中的主要问题形式之一，其中，仅有一个目标函数的最优化问题被称为单目标优化问题，目标函数超过一个并且需要同时处理的最优化问题被称为多目标优化问题（Multi-objective Optimization Problems,MOPs）。本论文对其前沿方向之一——人工免疫多目标优化模型进行了深入分析，针对高维多目标优化问题、新型支配机制、自适应克隆策略、高效的多样性保持技巧等具有挑战性的问题进行了深入的研究，结合国家自然科学基金项目、国家“973”项目和“863”计划等，将提出的方法成功用于合成孔径雷达(SyntheticAperture Radar, SAR)图像分割难题。本论文工作可以概括如下：
(1)目标维数较高的多目标优化问题的难点在于，随着非支配解急剧增加，经典算法由于缺乏足够的选择压力导致性能急剧下降。为此，提出了基于偏好等级免疫记忆的克隆选择多目标优化算法，用于解决目标维数较高的优化问题。利用决策者提供的偏好信息来为抗体分配偏好等级，根据该值比例克隆抗体，增大抗体的选择压力，加快收敛速率。根据偏好信息来缩减Pareto前沿，并利用有限的偏好解估计该前沿。同时，建立了免疫记忆种群来保留较好的非支配解，采用ε支配机制来保持记忆抗体种群的多样性。实验结果表明，对于高达8目标的优化问题，该算法取得了较好的实验效果。
(2)新型支配机制的研究是进化多目标优化领域中的热点和难点之一，其中，ε支配最具代表性。但是，它的缺点在于对于不同几何形状Pareto前沿的问题，其性能十分敏感。本论文提出了改进ε支配机制的等度规映射方法，采用等度规映射把解映射到低维流形空间，发现隐藏于非支配解的几何分布，在该空间进行ε支配的剪枝操作。与传统的ε支配相比，该机制不会丢失部分有效解，能够较好地保持解分布的均匀性。为克服传统ε支配丢失部分极端解的不足，设计了极端解校验算子。与四个代表的相关算法相比，本文提出的ε支配和极端解校验算子能够较好地保持解分布的均匀性和宽广性，明显地改进了传统ε支配。
(3)算法的效率与进化搜索过程的自适应性操作密切相关。传统算法多一成不变地为所有个体建立非支配等级关系，带来了计算资源的浪费。本论文研究了基于在线非支配抗体的自适应多目标优化算法，该算法根据当前的非支配抗体数量，把搜索阶段划分为三个不同时期，它们分别具有局部搜索和全局搜索以及混合搜索特性。当需要执行全局搜索时，为所有个体建立非支配等级关系；当非支配抗体较多时，即需要局部搜索时，可以完全抛弃支配抗体，仅仅选取非支配抗体参与操作。此外，为了较好地完成局部搜索，本章还设计一个基于比例克隆的局部增强搜索算法。本章算法明显提高了搜索资源的利用率，增强了算法的自适应性和有效性，克服了恒定地为所有个体排序的缺点。
(4)如何获得稳定而高效的免疫多目标优化算法？针对当前免疫多目标优化算法搜索过程的不稳定和多样性保持质量不高等缺点，提出了基于自适应等级克隆和动态删除机制的高效免疫多目标优化算法。自适应地选择个体和分配克隆资源增强了种群进化的鲁棒性，克服了传统算法陷入局部Pareto前沿的缺点。个体的近邻信息会随着个体的删除而更新，传统拥挤距离一次分配机制的多样性指标具有很大缺陷，而动态的分配机制可以明显克服该缺点，利于较好地保持解的多样性。本章算法在收敛性、多样性保持和运算时间均获得满意的实验结果。
(5)针对当前单目标SAR图像分割中遇到的单个聚类指标难于发现复杂像素分布关系的缺点，结合多通道Gabor滤波和灰度共生矩阵对SAR图像中的纹理信息进行描述，首次提出了一种基于免疫多目标优化的SAR图像分割方法。多目标优化可以揭示复杂样本特点，对具有不同几何结构的分类问题均有较好的划分性能。采用本论文第五章提出的高效人工免疫多目标优化算法作为本章SAR图像精细分割算法依托框架。对于部分纹理图像和ERS-2卫星图像取得了满意的分割结果。实验结果展示了免疫多目标计算在SAR图像分割中的应用潜力。
(6)针对当前基于进化计算的SAR图像自动分割算法的稳定性较差和现有多目标分割算法的效率不高等缺点，提出了高效的人工免疫多目标像素域SAR图像自动分割算法。多目标分割本质上是离散的两目标优化问题，为此，针对性地采用自适应等级均匀克隆机制和动态拥挤距离的删除机制，后者虽然对于目标维数较高的优化问题的多样性保持能力不足，但是对于两目标优化问题，其计算量相对较小，且具有满意的多样性保持性能。此外，为了获得分割图像在细节方面的清晰识别，本章采用更为直接的SAR图像斑点噪声抑制算法，目的在于滤除斑点噪声的同时，保留更多的纹理和边缘等细节信息，有利于感兴趣目标的解译和识别。对类别数目较多的合成SAR图像和TerraSAR卫星图像取得了明显较好的分割结果。
The optimal problem is a fundamental issue in current engineering practice andscientific research. Thereinto, if there is only one objective function in this kind ofproblem, it is the single-objective optimization problems (SOPs), and when the numberof objective function is larger than one, it is the essential form of multi-objectiveoptimization problems (MOPs). This thesis is to focus on the buildings of the advancedmodel and theory of artificial immune system based multi-objective optimization, whichis the leading and promising field in the area of MOPs. The intensive study has beenimplemented in the following challenging subjects, including many-objectiveoptimization problems, new dominance scheme, adaptive clone strategy, and efficienttechniques in diversity maintaining. Finally, the advanced techniques and proposedimmune multi-objective optimization algorithms are successfully applied into SyntheticAperture Radar (SAR) image segmentation. The main contributions of the thesis can besummarized as follows:
(1) The difficulty of current multi-objective optimization community lies in thelarge number of objectives. Due to lack enough selection pressure toward the Paretofront, classical algorithms are greatly restrained. To this end, an immune memory cloneselection algorithm is proposed to solve the problem of multi-objective optimizationwith a large number of objectives. The nondominated antibodies are proportionallycloned by their preference ranks, which are defined by their preference information. It isbeneficial to increase selection pressure and speed up convergence to the truePareto-optimal front. Solutions used to approximate the Pareto front can be reducedgreatly by preference information. Besides, an immune memory population is built topreserve the nondominated antibodies and ε dominance is employed to maintain thediversity of the immune memory population. Finally, the proposed algorithm performedeffective in testing several multi-objective problems with2objectives and DTLZ2andDTLZ3as high as8objectives.
(2) The study of new types of dominance mechanisms is a hot and key issue incurrent EMO community, and ε dominance is a representative one among them.However, their ability in diversity maintaining is sensitive to different shapes of Paretofronts. This paper proposes an improved ε dominance mechanism by Isomap, whichemploys Isomap to embed the original population to low dimensional manifold space. The intrinsic geometric structure of them could be discovered and ε dominance isadopted to select data in the embedding space. Compared with traditional ε dominance,the mechanism does not lose valid solutions and can maintain a set ofuniform-distributed solutions. Besides, extreme-solution-check operator is proposed toenhance the ability of holding extreme solutions of ε dominance. The detailedexperimental comparison with NSGAII, SPEA2, NNIA and εMOEA shows that the twostrategies in this study are beneficial to uniformity and spread maintaining.
(3) The efficiency of MO algorithms is highly related with the adaptability in thesearching process. Most traditional algorithms are always to assign all the solutions incurrent population to different ranks, which will induce the waste of computationalresources. An adaptive multi-objective optimization algorithm by online discoverednondominated solutions is presented for MOPs in our thesis. Here, three search phasesare devised according to the number of nondominated solutions in current population. Ifcurrent population contains very few nondominated solutions, global searching processis required and all the solutions need to build the dominance relations among them;when the population consists of adequate nondominated solutions, dominated onescould be ignored and the isolated nondominated ones should be allocated morecomputational budget for local search. To exploit local information efficiently, a localincremental search algorithm is proposed and merged into the model. This proposedalgorithm maintains the adaptive mechanism between the optimization processes by theonline discovered nondominated solutions, which has enhanced adaptability androbustness of the searching process.
(4) How to devise a steady and high-powered MO algorithm? Here, we proposedan immune MO algorithm based on adaptive ranks clone and dynamic deleting scheme,in consideration of the searching process of current immune MO algorithm beingsensitive to the number of nondominated solutions and poor performance of diversitymaintaining. The adaptive selection scheme and adaptive ranks clone scheme by theonline discovered solutions in different ranks can enhance the robustness of theproposed algorithm, which is less possible to be trapped into local optimal Pareto front.Furthermore, it has been widely approved that one-off deletion could not obtainexcellent diversity in the final population; therefore, a m-nearest neighbor list (where mis the number of objectives) is established and maintained to eliminate the solutions inthe archive population. Finally, the proposed algorithm achieved satisfied results interms of convergence, diversity metrics, and computational time.
(5) Considering the bad performance of SO with single clustering validity index indiscovering the complicated relations among image pixels, an artificial immunemulti-objective optimization framework is formulated for the first time and applied toSAR image segmentation. Moreover, a fused feature set for texture representation isconstructed and researched, which utilizes both the Gabor filter’s ability to preciselyextract texture features in low-and mid-frequency components and the gray levelco-occurrence probability’s (GLCP) ability to measure information in high-frequency.MO algorithms can discover the complicated relations among image pixels and have thegood partitioning performance in clustering the classification problems with differentgeometrical structures. Besides, the efficient and robust MO algorithm in AIS proposedin Chapter5of the thesis is used as the framework of the SAR image fine segmentation.Finally, satisfactory segmentation results of several texture images and SAR imagesfrom ERS-2satellite are obtained by the proposed method. The experimental resultsshow its great potential application in SAR image segmentation.
(6) In view of weak performance of existing SAR image segmentation bysingle-objective evolutionary algorithms and very low efficiency of currentmulti-objective segmentation algorithms. A high efficient multi-objective automaticSAR segmentation algorithm in AIS is proposed in this chapter. It is essentially discretetwo-objective optimization problems for current SAR image segmentation. With this inmind, the multi-objective SAR image segmentation employ adaptive rank baseduniform clone and dynamic crowding distance deletion. The later scheme has beenviewed as poor performance in diversity maintaining for many-objective optimization,however it can obtain satisfied results for two-objective optimization and takes on lesscomputational complexity. Besides, SAR image filtering method is employed in theimage preprocessing stage in order to preserve the fine structure, details and texture forsubsequent objective of interest recognition and better image understudying. Theproposed algorithm obtained the obvious better partitioning results in segmenting twoartificial SAR images with many number of categories and real SAR images fromTerraSAR satellite.
(1)目标维数较高的多目标优化问题的难点在于，随着非支配解急剧增加，经典算法由于缺乏足够的选择压力导致性能急剧下降。为此，提出了基于偏好等级免疫记忆的克隆选择多目标优化算法，用于解决目标维数较高的优化问题。利用决策者提供的偏好信息来为抗体分配偏好等级，根据该值比例克隆抗体，增大抗体的选择压力，加快收敛速率。根据偏好信息来缩减Pareto前沿，并利用有限的偏好解估计该前沿。同时，建立了免疫记忆种群来保留较好的非支配解，采用ε支配机制来保持记忆抗体种群的多样性。实验结果表明，对于高达8目标的优化问题，该算法取得了较好的实验效果。
(2)新型支配机制的研究是进化多目标优化领域中的热点和难点之一，其中，ε支配最具代表性。但是，它的缺点在于对于不同几何形状Pareto前沿的问题，其性能十分敏感。本论文提出了改进ε支配机制的等度规映射方法，采用等度规映射把解映射到低维流形空间，发现隐藏于非支配解的几何分布，在该空间进行ε支配的剪枝操作。与传统的ε支配相比，该机制不会丢失部分有效解，能够较好地保持解分布的均匀性。为克服传统ε支配丢失部分极端解的不足，设计了极端解校验算子。与四个代表的相关算法相比，本文提出的ε支配和极端解校验算子能够较好地保持解分布的均匀性和宽广性，明显地改进了传统ε支配。
(3)算法的效率与进化搜索过程的自适应性操作密切相关。传统算法多一成不变地为所有个体建立非支配等级关系，带来了计算资源的浪费。本论文研究了基于在线非支配抗体的自适应多目标优化算法，该算法根据当前的非支配抗体数量，把搜索阶段划分为三个不同时期，它们分别具有局部搜索和全局搜索以及混合搜索特性。当需要执行全局搜索时，为所有个体建立非支配等级关系；当非支配抗体较多时，即需要局部搜索时，可以完全抛弃支配抗体，仅仅选取非支配抗体参与操作。此外，为了较好地完成局部搜索，本章还设计一个基于比例克隆的局部增强搜索算法。本章算法明显提高了搜索资源的利用率，增强了算法的自适应性和有效性，克服了恒定地为所有个体排序的缺点。
(4)如何获得稳定而高效的免疫多目标优化算法？针对当前免疫多目标优化算法搜索过程的不稳定和多样性保持质量不高等缺点，提出了基于自适应等级克隆和动态删除机制的高效免疫多目标优化算法。自适应地选择个体和分配克隆资源增强了种群进化的鲁棒性，克服了传统算法陷入局部Pareto前沿的缺点。个体的近邻信息会随着个体的删除而更新，传统拥挤距离一次分配机制的多样性指标具有很大缺陷，而动态的分配机制可以明显克服该缺点，利于较好地保持解的多样性。本章算法在收敛性、多样性保持和运算时间均获得满意的实验结果。
(5)针对当前单目标SAR图像分割中遇到的单个聚类指标难于发现复杂像素分布关系的缺点，结合多通道Gabor滤波和灰度共生矩阵对SAR图像中的纹理信息进行描述，首次提出了一种基于免疫多目标优化的SAR图像分割方法。多目标优化可以揭示复杂样本特点，对具有不同几何结构的分类问题均有较好的划分性能。采用本论文第五章提出的高效人工免疫多目标优化算法作为本章SAR图像精细分割算法依托框架。对于部分纹理图像和ERS-2卫星图像取得了满意的分割结果。实验结果展示了免疫多目标计算在SAR图像分割中的应用潜力。
(6)针对当前基于进化计算的SAR图像自动分割算法的稳定性较差和现有多目标分割算法的效率不高等缺点，提出了高效的人工免疫多目标像素域SAR图像自动分割算法。多目标分割本质上是离散的两目标优化问题，为此，针对性地采用自适应等级均匀克隆机制和动态拥挤距离的删除机制，后者虽然对于目标维数较高的优化问题的多样性保持能力不足，但是对于两目标优化问题，其计算量相对较小，且具有满意的多样性保持性能。此外，为了获得分割图像在细节方面的清晰识别，本章采用更为直接的SAR图像斑点噪声抑制算法，目的在于滤除斑点噪声的同时，保留更多的纹理和边缘等细节信息，有利于感兴趣目标的解译和识别。对类别数目较多的合成SAR图像和TerraSAR卫星图像取得了明显较好的分割结果。
The optimal problem is a fundamental issue in current engineering practice andscientific research. Thereinto, if there is only one objective function in this kind ofproblem, it is the single-objective optimization problems (SOPs), and when the numberof objective function is larger than one, it is the essential form of multi-objectiveoptimization problems (MOPs). This thesis is to focus on the buildings of the advancedmodel and theory of artificial immune system based multi-objective optimization, whichis the leading and promising field in the area of MOPs. The intensive study has beenimplemented in the following challenging subjects, including many-objectiveoptimization problems, new dominance scheme, adaptive clone strategy, and efficienttechniques in diversity maintaining. Finally, the advanced techniques and proposedimmune multi-objective optimization algorithms are successfully applied into SyntheticAperture Radar (SAR) image segmentation. The main contributions of the thesis can besummarized as follows:
(1) The difficulty of current multi-objective optimization community lies in thelarge number of objectives. Due to lack enough selection pressure toward the Paretofront, classical algorithms are greatly restrained. To this end, an immune memory cloneselection algorithm is proposed to solve the problem of multi-objective optimizationwith a large number of objectives. The nondominated antibodies are proportionallycloned by their preference ranks, which are defined by their preference information. It isbeneficial to increase selection pressure and speed up convergence to the truePareto-optimal front. Solutions used to approximate the Pareto front can be reducedgreatly by preference information. Besides, an immune memory population is built topreserve the nondominated antibodies and ε dominance is employed to maintain thediversity of the immune memory population. Finally, the proposed algorithm performedeffective in testing several multi-objective problems with2objectives and DTLZ2andDTLZ3as high as8objectives.
(2) The study of new types of dominance mechanisms is a hot and key issue incurrent EMO community, and ε dominance is a representative one among them.However, their ability in diversity maintaining is sensitive to different shapes of Paretofronts. This paper proposes an improved ε dominance mechanism by Isomap, whichemploys Isomap to embed the original population to low dimensional manifold space. The intrinsic geometric structure of them could be discovered and ε dominance isadopted to select data in the embedding space. Compared with traditional ε dominance,the mechanism does not lose valid solutions and can maintain a set ofuniform-distributed solutions. Besides, extreme-solution-check operator is proposed toenhance the ability of holding extreme solutions of ε dominance. The detailedexperimental comparison with NSGAII, SPEA2, NNIA and εMOEA shows that the twostrategies in this study are beneficial to uniformity and spread maintaining.
(3) The efficiency of MO algorithms is highly related with the adaptability in thesearching process. Most traditional algorithms are always to assign all the solutions incurrent population to different ranks, which will induce the waste of computationalresources. An adaptive multi-objective optimization algorithm by online discoverednondominated solutions is presented for MOPs in our thesis. Here, three search phasesare devised according to the number of nondominated solutions in current population. Ifcurrent population contains very few nondominated solutions, global searching processis required and all the solutions need to build the dominance relations among them;when the population consists of adequate nondominated solutions, dominated onescould be ignored and the isolated nondominated ones should be allocated morecomputational budget for local search. To exploit local information efficiently, a localincremental search algorithm is proposed and merged into the model. This proposedalgorithm maintains the adaptive mechanism between the optimization processes by theonline discovered nondominated solutions, which has enhanced adaptability androbustness of the searching process.
(4) How to devise a steady and high-powered MO algorithm? Here, we proposedan immune MO algorithm based on adaptive ranks clone and dynamic deleting scheme,in consideration of the searching process of current immune MO algorithm beingsensitive to the number of nondominated solutions and poor performance of diversitymaintaining. The adaptive selection scheme and adaptive ranks clone scheme by theonline discovered solutions in different ranks can enhance the robustness of theproposed algorithm, which is less possible to be trapped into local optimal Pareto front.Furthermore, it has been widely approved that one-off deletion could not obtainexcellent diversity in the final population; therefore, a m-nearest neighbor list (where mis the number of objectives) is established and maintained to eliminate the solutions inthe archive population. Finally, the proposed algorithm achieved satisfied results interms of convergence, diversity metrics, and computational time.
(5) Considering the bad performance of SO with single clustering validity index indiscovering the complicated relations among image pixels, an artificial immunemulti-objective optimization framework is formulated for the first time and applied toSAR image segmentation. Moreover, a fused feature set for texture representation isconstructed and researched, which utilizes both the Gabor filter’s ability to preciselyextract texture features in low-and mid-frequency components and the gray levelco-occurrence probability’s (GLCP) ability to measure information in high-frequency.MO algorithms can discover the complicated relations among image pixels and have thegood partitioning performance in clustering the classification problems with differentgeometrical structures. Besides, the efficient and robust MO algorithm in AIS proposedin Chapter5of the thesis is used as the framework of the SAR image fine segmentation.Finally, satisfactory segmentation results of several texture images and SAR imagesfrom ERS-2satellite are obtained by the proposed method. The experimental resultsshow its great potential application in SAR image segmentation.
(6) In view of weak performance of existing SAR image segmentation bysingle-objective evolutionary algorithms and very low efficiency of currentmulti-objective segmentation algorithms. A high efficient multi-objective automaticSAR segmentation algorithm in AIS is proposed in this chapter. It is essentially discretetwo-objective optimization problems for current SAR image segmentation. With this inmind, the multi-objective SAR image segmentation employ adaptive rank baseduniform clone and dynamic crowding distance deletion. The later scheme has beenviewed as poor performance in diversity maintaining for many-objective optimization,however it can obtain satisfied results for two-objective optimization and takes on lesscomputational complexity. Besides, SAR image filtering method is employed in theimage preprocessing stage in order to preserve the fine structure, details and texture forsubsequent objective of interest recognition and better image understudying. Theproposed algorithm obtained the obvious better partitioning results in segmenting twoartificial SAR images with many number of categories and real SAR images fromTerraSAR satellite.
引文
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