南方科技大学知识苑(SUSTech KC): 演化多目标优化中支配抵抗解的影响及应对策略研究

题名	演化多目标优化中支配抵抗解的影响及应对策略研究
其他题名	RESEARCH ON THE INFLUENCE OF DOMINANCE-RESISTANT SOLUTIONS AND COPING STRATEGIES IN EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION
姓名	林康年
姓名拼音	LIN Kangnian
学号	12132672
学位类型	硕士
学位专业	0801Z1 智能制造与机器人
学科门类/专业学位类别	08 工学
导师	王振坤
导师单位	系统设计与智能制造学院
论文答辩日期	2024-05-10
论文提交日期	2024-06-28
学位授予单位	南方科技大学
学位授予地点	深圳
摘要	多目标优化问题在实际优化应用场景中是一类常见的问题，而演化算法则是目前处理这类问题的主流方法。然而，近年来随着理论研究和实际生产的发展，一些复杂特征逐渐显现在多目标优化问题中，导致算法在处理具有这些特征的问题时性能出现不同程度的下降。其中，支配抵抗解是一种重要而常见的复杂特征，其主要位于问题可行域的特殊边界，即难支配边界上。然而，在理论研究中，许多常见测试问题通常未充分考虑到这种特征，因而未能对支配抵抗解在演化多目标优化过程中产生的影响进行全面和综合的分析。与此同时，尽管目前已有一些算法专注于解决支配抵抗解的影响，但它们仍然存在着各自的不足之处。本文旨在深入分析支配抵抗解及其对演化多目标优化过程的影响，并在此基础上提出更加充分考虑问题特征的应对策略。在现有基准测试问题基础上，本文开发了一系列新的测试问题，这些问题综合考虑了支配抵抗解及多个实际问题特征，使得测试问题的深度和广度得到了进一步的丰富，并且更符合实际应用场景。同时，通过在开发的测试问题上进行算例分析，本文对目前一些处理支配抵抗解的代表性算法的性能进行详尽检验，以深入分析支配抵抗解以及相关特征对演化过程的影响。最后，根据算例分析的结论，本文根据问题的特征提出了一系列具有针对性的支配抵抗解应对策略。算例结果明确展示了提出算法在性能上相对于现有应对策略的优势，同时也揭示了算法各部件的有效性及其运行机理。本文的研究内容与成果对于理解和应对支配抵抗解在多目标优化问题中的影响能够提供一定的有力支持。
其他摘要	Multi-objective optimization problems are a common category in practical optimization applications, and evolutionary algorithms currently stand as the mainstream approach for tackling such issues. However, in recent years, with the development of theoretical research and practical production, some intricate features have gradually surfaced in multiobjective optimization problems, leading to varying degrees of performance degradation when algorithms handle problems with these features. Dominance-resistant solutions represent one significant and prevalent intricate feature, and they mainly locate on the special boundaries on the feasible region of problem, namely hardly dominated boundaries. However, in theoretical research, many commonly used test problems often fail to adequately consider this feature, thereby lacking a comprehensive and thorough analysis of the impact of dominance-resistant solutions during the evolutionary multi-objective optimization process. Meanwhile, although there are algorithms currently dedicated to addressing the impact of dominance-resistant solutions, they still have their own shortcomings. This paper aims to delve into the analysis of dominance-resistant solutions and their effects on the evolutionary multi-objective optimization process, proposing responsive strategies that take into account the characteristics of the problems in question. Building upon existing benchmark problems, this study has developed a series of novel test problems that comprehensively consider dominance-resistant solutions and multiple real-world problem features. This enhancement contributes to a more profound and extensive set of test problems, better aligning with practical application scenarios. Simultaneously, through experiments conducted on the developed test problems, this paper rigorously examines the performance of representative algorithms for handling dominance-resistant solutions, providing an in-depth analysis of the impact of dominance-resistant solutions and related features on the evolutionary process. Finally, based on the conclusions drawn from experimental analysis, the paper proposes a series of targeted strategies for dealing with dominance-resistant solutions, taking into account the specific characteristics of the problems. The experimental results distinctly showcase the advantages of the proposed algorithm in performance compared to existing strategies, while also revealing the effectiveness of individual components and their operational mechanisms. The research content and findings of this paper provide substantial support for understanding and addressing the impact of dominance-resistant solutions in multi-objective optimization problems.
关键词	多目标优化问题演化算法支配抵抗解难支配边界
其他关键词	Multi-objective Optimization Problem Evolutionary Algorithm Dominance Resistant Solution Hardly Dominated Boundary
语种	中文
培养类别	独立培养
入学年份	2021
学位授予年份	2024-06
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专题	工学院_系统设计与智能制造学院
推荐引用方式 GB/T 7714	林康年. 演化多目标优化中支配抵抗解的影响及应对策略研究[D]. 深圳. 南方科技大学,2024.

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