{"title":"基于对立学习的自适应混沌粒子群优化算法","authors":"Chongyang Jiao, Kunjie Yu, Qinglei Zhou","doi":"10.1007/s42235-024-00578-4","DOIUrl":null,"url":null,"abstract":"<div><p>To solve the shortcomings of Particle Swarm Optimization (PSO) algorithm, local optimization and slow convergence, an Opposition-based Learning Adaptive Chaotic PSO (LCPSO) algorithm was presented. The chaotic elite opposition-based learning process was applied to initialize the entire population, which enhanced the quality of the initial individuals and the population diversity, made the initial individuals distribute in the better quality areas, and accelerated the search efficiency of the algorithm. The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm, and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum. The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics, and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability, search accuracy and convergence speed. In addition, the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"21 6","pages":"3076 - 3097"},"PeriodicalIF":4.9000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm\",\"authors\":\"Chongyang Jiao, Kunjie Yu, Qinglei Zhou\",\"doi\":\"10.1007/s42235-024-00578-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To solve the shortcomings of Particle Swarm Optimization (PSO) algorithm, local optimization and slow convergence, an Opposition-based Learning Adaptive Chaotic PSO (LCPSO) algorithm was presented. The chaotic elite opposition-based learning process was applied to initialize the entire population, which enhanced the quality of the initial individuals and the population diversity, made the initial individuals distribute in the better quality areas, and accelerated the search efficiency of the algorithm. The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm, and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum. The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics, and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability, search accuracy and convergence speed. In addition, the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.</p></div>\",\"PeriodicalId\":614,\"journal\":{\"name\":\"Journal of Bionic Engineering\",\"volume\":\"21 6\",\"pages\":\"3076 - 3097\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Bionic Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s42235-024-00578-4\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bionic Engineering","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s42235-024-00578-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm
To solve the shortcomings of Particle Swarm Optimization (PSO) algorithm, local optimization and slow convergence, an Opposition-based Learning Adaptive Chaotic PSO (LCPSO) algorithm was presented. The chaotic elite opposition-based learning process was applied to initialize the entire population, which enhanced the quality of the initial individuals and the population diversity, made the initial individuals distribute in the better quality areas, and accelerated the search efficiency of the algorithm. The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm, and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum. The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics, and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability, search accuracy and convergence speed. In addition, the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.
期刊介绍:
The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to:
Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion.
Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials.
Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices.
Development of bioinspired computation methods and artificial intelligence for engineering applications.