Constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-03-14 DOI:10.1016/j.swevo.2025.101903

Jianxia Li, Ruochen Liu, Xilong Zhang, Ruinan Wang

{"title":"Constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints","authors":"Jianxia Li, Ruochen Liu, Xilong Zhang, Ruinan Wang","doi":"10.1016/j.swevo.2025.101903","DOIUrl":null,"url":null,"abstract":"<div><div>Many engineering optimization problems require simultaneous optimization of multiple objective functions under certain constraints, which are collectively referred to as constrained multi-objective problems (CMOPs). The crucial issue in solving CMOPs is to balance constraints and objectives. This paper proposes a constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints, termed CORCMO. CORCMO mainly comprises two stages: the learning stage and the evolving stage. The learning stage focuses on analyzing the correlation between each objective and constraints. In the evolving stage, the CMOP is decomposed into <em>M</em> constraint single-objective problems, which are optimized by <em>M</em> subpopulations cooperatively. For each subproblem, the corresponding fitness function, computed based on the correlation, is adopted to guide the evolution. Subsequently, CORCMO employs archive population update strategy to find the optimal solutions of the given CMOP. Experiments conducted on a series of benchmark problems demonstrate that CORCMO is promising to solve CMOPs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101903"},"PeriodicalIF":8.2000,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225000616","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Many engineering optimization problems require simultaneous optimization of multiple objective functions under certain constraints, which are collectively referred to as constrained multi-objective problems (CMOPs). The crucial issue in solving CMOPs is to balance constraints and objectives. This paper proposes a constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints, termed CORCMO. CORCMO mainly comprises two stages: the learning stage and the evolving stage. The learning stage focuses on analyzing the correlation between each objective and constraints. In the evolving stage, the CMOP is decomposed into M constraint single-objective problems, which are optimized by M subpopulations cooperatively. For each subproblem, the corresponding fitness function, computed based on the correlation, is adopted to guide the evolution. Subsequently, CORCMO employs archive population update strategy to find the optimal solutions of the given CMOP. Experiments conducted on a series of benchmark problems demonstrate that CORCMO is promising to solve CMOPs.

查看原文本刊更多论文

基于目标与约束条件相关性的约束多目标进化算法

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.