{"title":"混沌粒子群优化算法的收敛速度研究","authors":"Bhanu Sekhar Obbu, Zmrooda Jabeen","doi":"10.1556/606.2023.00933","DOIUrl":null,"url":null,"abstract":"Abstract This study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.","PeriodicalId":35003,"journal":{"name":"Pollack Periodica","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study of convergence speed of chaotic particle swarm optimization algorithm\",\"authors\":\"Bhanu Sekhar Obbu, Zmrooda Jabeen\",\"doi\":\"10.1556/606.2023.00933\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.\",\"PeriodicalId\":35003,\"journal\":{\"name\":\"Pollack Periodica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pollack Periodica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1556/606.2023.00933\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pollack Periodica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1556/606.2023.00933","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Study of convergence speed of chaotic particle swarm optimization algorithm
Abstract This study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.
期刊介绍:
Pollack Periodica is an interdisciplinary, peer-reviewed journal that provides an international forum for the presentation, discussion and dissemination of the latest advances and developments in engineering and informatics. Pollack Periodica invites papers reporting new research and applications from a wide range of discipline, including civil, mechanical, electrical, environmental, earthquake, material and information engineering. The journal aims at reaching a wider audience, not only researchers, but also those likely to be most affected by research results, for example designers, fabricators, specialists, developers, computer scientists managers in academic, governmental and industrial communities.