{"title":"约束景观知识辅助约束多目标优化","authors":"Yuhang Ma , Bo Shen , Anqi Pan , Jiankai Xue","doi":"10.1016/j.swevo.2024.101685","DOIUrl":null,"url":null,"abstract":"<div><p>When evolutionary algorithms are employed to tackle constrained multiobjective optimization problems (CMOPs), constraint handling techniques (CHTs) play a pivotal role. To date, several CHTs have been designed, but they are only effective for certain types of constraint landscapes. For CMOPs with unknown properties, their optimization performance and efficiency remain uncertain. To tackle this issue, we attempt to mine and utilize the knowledge of constraint landscape to solve CMOPs. Specifically, the evolutionary process can be divided into three stages: learning stage, classification stage, and evolving stage. During the learning stage, the two populations, namely <em>mainPop</em> and <em>auxPop</em>, cooperatively evolve with and without considering constraints, respectively. The <em>mainPop</em> can locate the feasible regions, while the <em>auxPop</em> is employed to evaluate the size of the feasible regions. Subsequently, in the classification stage, based on the learned landscape knowledge, the category of problem can be determined: CMOP with small feasible regions or CMOP with large feasible regions. Then, in the evolving stage, for CMOPs with small feasible regions, CHTI, which includes a population exchange method and a feasible regions relaxation method, is proposed, while for CMOPs with large feasible regions, CHTII, which encompasses a dynamic resource allocation method and a coevolutionary method, is designed. The proposed framework is executed on extensive benchmark test suites. It has achieved superior or at least competitive performance compared with other state-of-the-art algorithms. Furthermore, the framework has been successfully implemented on the robotic manipulator path planning problem.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101685"},"PeriodicalIF":8.2000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Constraint landscape knowledge assisted constrained multiobjective optimization\",\"authors\":\"Yuhang Ma , Bo Shen , Anqi Pan , Jiankai Xue\",\"doi\":\"10.1016/j.swevo.2024.101685\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>When evolutionary algorithms are employed to tackle constrained multiobjective optimization problems (CMOPs), constraint handling techniques (CHTs) play a pivotal role. To date, several CHTs have been designed, but they are only effective for certain types of constraint landscapes. For CMOPs with unknown properties, their optimization performance and efficiency remain uncertain. To tackle this issue, we attempt to mine and utilize the knowledge of constraint landscape to solve CMOPs. Specifically, the evolutionary process can be divided into three stages: learning stage, classification stage, and evolving stage. During the learning stage, the two populations, namely <em>mainPop</em> and <em>auxPop</em>, cooperatively evolve with and without considering constraints, respectively. The <em>mainPop</em> can locate the feasible regions, while the <em>auxPop</em> is employed to evaluate the size of the feasible regions. Subsequently, in the classification stage, based on the learned landscape knowledge, the category of problem can be determined: CMOP with small feasible regions or CMOP with large feasible regions. Then, in the evolving stage, for CMOPs with small feasible regions, CHTI, which includes a population exchange method and a feasible regions relaxation method, is proposed, while for CMOPs with large feasible regions, CHTII, which encompasses a dynamic resource allocation method and a coevolutionary method, is designed. The proposed framework is executed on extensive benchmark test suites. It has achieved superior or at least competitive performance compared with other state-of-the-art algorithms. Furthermore, the framework has been successfully implemented on the robotic manipulator path planning problem.</p></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":\"90 \",\"pages\":\"Article 101685\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-08-09\",\"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/S2210650224002232\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650224002232","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}
When evolutionary algorithms are employed to tackle constrained multiobjective optimization problems (CMOPs), constraint handling techniques (CHTs) play a pivotal role. To date, several CHTs have been designed, but they are only effective for certain types of constraint landscapes. For CMOPs with unknown properties, their optimization performance and efficiency remain uncertain. To tackle this issue, we attempt to mine and utilize the knowledge of constraint landscape to solve CMOPs. Specifically, the evolutionary process can be divided into three stages: learning stage, classification stage, and evolving stage. During the learning stage, the two populations, namely mainPop and auxPop, cooperatively evolve with and without considering constraints, respectively. The mainPop can locate the feasible regions, while the auxPop is employed to evaluate the size of the feasible regions. Subsequently, in the classification stage, based on the learned landscape knowledge, the category of problem can be determined: CMOP with small feasible regions or CMOP with large feasible regions. Then, in the evolving stage, for CMOPs with small feasible regions, CHTI, which includes a population exchange method and a feasible regions relaxation method, is proposed, while for CMOPs with large feasible regions, CHTII, which encompasses a dynamic resource allocation method and a coevolutionary method, is designed. The proposed framework is executed on extensive benchmark test suites. It has achieved superior or at least competitive performance compared with other state-of-the-art algorithms. Furthermore, the framework has been successfully implemented on the robotic manipulator path planning problem.
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.