Multiobjective Optimization Problem With Hardly Dominated Boundaries: Benchmark, Analysis, and Indicator-Based Algorithm

IF 11.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Evolutionary Computation Pub Date : 2024-03-20 DOI:10.1109/TEVC.2024.3403414

Zhenkun Wang;Kangnian Lin;Genghui Li;Weifeng Gao

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Abstract

The hardly dominated boundary (HDB) is commonly observed in multiobjective optimization problems (HDB-MOPs). However, there are only a few benchmark problems related to HDB-MOPs in the evolutionary computation community, which is insufficient to validate the performance of the multiobjective evolutionary algorithms (MOEAs). In this article, we first introduce a new set of HDB-MOPs characterized by various shapes of Pareto fronts and scalable HDB sizes. We then systematically analyze the capabilities of several representative existing MOEAs in handling HDB-MOPs and reveal their strengths and weaknesses in solving this type of problem. Finally, based on this insightful analysis, we propose an indicator-based MOEA with an adaptive reference point (denoted as IMOEA-ARP) to effectively address HDB-MOPs. The source codes of the proposed benchmark problems and the IMOEA-ARP algorithm are available from https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/IMOEA-ARP.

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边界难以控制的多目标优化问题：基准、分析和基于指标的算法

在多目标优化问题（HDB- mops）中，难以支配边界（HDB- mops）是一个常见的问题。然而，在进化计算界，与HDB-MOPs相关的基准测试问题很少，不足以验证多目标进化算法（moea）的性能。在本文中，我们首先介绍一组新的HDB- mops，其特征是各种形状的Pareto front和可扩展的HDB大小。然后，我们系统地分析了几个具有代表性的现有moea在处理HDB-MOPs方面的能力，并揭示了它们在解决此类问题方面的优势和劣势。最后，基于这一深刻的分析，我们提出了一个基于指标的MOEA和一个自适应参考点（表示为IMOEA-ARP），以有效地解决HDB-MOPs。所提出的基准问题和IMOEA-ARP算法的源代码可从https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/IMOEA-ARP获得。

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来源期刊

IEEE Transactions on Evolutionary Computation 工程技术-计算机：理论方法

CiteScore

21.90

自引率

9.80%

发文量

196

审稿时长

3.6 months

期刊介绍： The IEEE Transactions on Evolutionary Computation is published by the IEEE Computational Intelligence Society on behalf of 13 societies: Circuits and Systems; Computer; Control Systems; Engineering in Medicine and Biology; Industrial Electronics; Industry Applications; Lasers and Electro-Optics; Oceanic Engineering; Power Engineering; Robotics and Automation; Signal Processing; Social Implications of Technology; and Systems, Man, and Cybernetics. The journal publishes original papers in evolutionary computation and related areas such as nature-inspired algorithms, population-based methods, optimization, and hybrid systems. It welcomes both purely theoretical papers and application papers that provide general insights into these areas of computation.