文章目录

• 1.论文摘要
• 2.正文部分
• • 问题介绍
  • 算法分类
  • 算法优缺点
  • 问题适应性
  • 不同算法的应用情况
• 3.总结

来自TEVC上最新的论文。
Title: A Survey on Evolutionary Constrained Multi-objective Optimization

Authors: Jing Liang; Xuanxuan Ban; Kunjie Yu; Boyang Qu; Kangjia Qiao; Caitong Yue; Ke Chen; Kay Chen Tan

Journal: IEEE Transactions on Evolutionary Computation

DOI: 10.1109/TEVC.2022.3155533

1.论文摘要

Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization.

处理受约束的多目标优化问题 (CMOP) 极具挑战性，因为需要同时优化多个受各种约束的冲突目标。为了处理 CMOP，近年来提出了许多受约束的多目标进化算法（CMOEA），并取得了可喜的性能。然而，目前对相关研究进行系统评价的文献很少。本文对进化约束多目标优化进行了全面调查。我们首先通过分类回顾了大量的 CMOEA，并分析了它们在每个类别中的优缺点。然后，我们总结了基准测试问题并研究了不同约束处理技术和不同算法的性能，然后是 CMOEA 的一些新兴和代表性应用。最后，我们讨论了一些新的挑战，并指出了进化约束多目标优化领域未来研究的一些方向。

2.正文部分

问题介绍

具体而言，在处理 CMPOPs [23] 时将遇到以下主要挑战：
1）可行性难度：如图2（a）所示，约束可能导致更大比例的搜索空间成为不可行区域。 因此，一个非常小的可行域很难定位。
2）收敛困难：如图2（b）所示，不可行区域可能成为通往CPF的障碍。 一个算法很容易陷入可行的外部区域，因此找到真正的CPF并不容易。
3）多样性的困难：如图2（c）所示，断开的可行区域将使真正的CPF由多个断开的

算法分类

算法优缺点

问题适应性

不同算法的应用情况

3.总结

This article reviews the research of evolutionary constrained multi-objective optimization, covering the basic concepts, existing algorithms, benchmark test functions, applications, and future research directions. Firstly, the relevant theoretical background and some concepts have been introduced. Secondly, this paper has reviewed the existing CMOEAs and divided them into seven categories according to their CHTs and internal mechanisms. Thirdly, some existing test function suites have been introduced and classified in detail, and the advantages and disadvantages of each test suite are summarized. Fourthly, some popular applications of evolutionary constrained multi-objective optimization have been introduced and summarized. Fifthly, the performance of different CHTs and CMOEAs on different types of problems is investigated. Finally, some future research directions have been discussed. It is hoped that the work presented in this article will help researchers to become familiar with evolutionary constrained multi-objective optimization, and promote the future development of this research direction.

本文回顾了进化约束多目标优化的研究，涵盖了基本概念、现有算法、基准测试功能、应用和未来的研究方向。首先介绍了相关的理论背景和一些概念。其次，本文回顾了现有的 CMOEAs，并根据 CHTs 和内部机制将它们分为七类。第三，对现有的一些测试功能套件进行了详细的介绍和分类，总结了各个测试套件的优缺点。第四，介绍和总结了进化约束多目标优化的一些流行应用。第五，研究了不同 CHT 和 CMOEA 在不同类型问题上的表现。最后，讨论了一些未来的研究方向。希望本文所介绍的工作能够帮助研究人员熟悉进化约束多目标优化，并推动该研究方向的未来发展。

标签：Multi,Evolutionary,some,multi,TEVC,算法,evolutionary,objective,constrained
来源： https://blog.csdn.net/weixin_39490300/article/details/123309010