{"title":"Differential evolution with ring sub-population architecture for optimization","authors":"","doi":"10.1016/j.knosys.2024.112590","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, evolutionary algorithms have achieved outstanding results in addressing increasingly complex optimization problems, with differential evolution (DE) gaining significant attention. However, due to its simple yet efficient evolutionary mechanism, DE has consistently faced challenges in mitigating the risk of premature convergence. This paper introduces a novel Ring Sub-population architecture-based Differential Evolution (RSDE) to address this issue. RSDE incorporates a conditional similarity selection mechanism that integrates multiple strategies. By considering fitness evaluation and population distribution, RSDE facilitates rich information exchange among sub-populations, leading to cyclic optimization. This global conditional interaction mechanism provides a new idea for population structure research, effectively preserves valuable solutions within the population, and prevents stagnation due to rapid convergence. The performance of RSDE is rigorously evaluated using 29 benchmark functions from the IEEE Congress on Evolutionary Computation (CEC) 2017, 22 real-world problems from CEC2011, and 12 complex optimization problems from CEC2022. RSDE is compared with 18 advanced algorithms, including leading DE variants and other state-of-the-art methods. The results demonstrate that the proposed RSDE algorithm performs well and is highly competitive with other competitors.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124012243","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
In recent years, evolutionary algorithms have achieved outstanding results in addressing increasingly complex optimization problems, with differential evolution (DE) gaining significant attention. However, due to its simple yet efficient evolutionary mechanism, DE has consistently faced challenges in mitigating the risk of premature convergence. This paper introduces a novel Ring Sub-population architecture-based Differential Evolution (RSDE) to address this issue. RSDE incorporates a conditional similarity selection mechanism that integrates multiple strategies. By considering fitness evaluation and population distribution, RSDE facilitates rich information exchange among sub-populations, leading to cyclic optimization. This global conditional interaction mechanism provides a new idea for population structure research, effectively preserves valuable solutions within the population, and prevents stagnation due to rapid convergence. The performance of RSDE is rigorously evaluated using 29 benchmark functions from the IEEE Congress on Evolutionary Computation (CEC) 2017, 22 real-world problems from CEC2011, and 12 complex optimization problems from CEC2022. RSDE is compared with 18 advanced algorithms, including leading DE variants and other state-of-the-art methods. The results demonstrate that the proposed RSDE algorithm performs well and is highly competitive with other competitors.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.