多目标粒子群优化算法及其应用研究综述  

Survey of multi-objective particle swarm optimization algorithms and their applications

作　　者：叶倩琳 王万良[1] 王铮 YE Qianlin;WANG Wanliang;WANG Zheng(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China;School of Computer and Computational Sciences,Hangzhou City University,Hangzhou 310015,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,浙江杭州310023 [2]浙大城市学院计算机与计算科学学院,浙江杭州310015

出　　处：《浙江大学学报（工学版）》2024年第6期1107-1120,1232,共15页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61873240,51875524);浙江省重点研发计划资助项目(领雁计划)(2023C01168);数字化制造装备与技术国家重点实验室基金资助项目(2023C01168)。

摘　　要：现有研究较少涵盖最先进的多目标粒子群优化(MOPSO)算法.本研究介绍了多目标优化问题(MOPs)的研究背景,阐述了MOPSO的基本理论.根据特征将其分为基于Pareto支配、基于分解和基于指标的3类MOPSO算法,介绍了现有的经典算法.介绍相关评价指标,并选取7个有代表性的算法进行性能分析.实验结果展示了传统MOPSO和3类改进的MOPSO算法各自的优势与不足,其中,基于指标的MOPSO在收敛性和多样性方面表现较优.对MOPSO算法在生产调度、图像处理和电力系统等领域的应用进行简要介绍.并探讨了MOPSO算法用于求解复杂优化问题的局限性及未来的研究方向.Few existing studies cover the state-of-the-art multi-objective particle swarm optimization(MOPSO)algorithms.To fill the gap in this area,the research background of multi-objective optimization problems(MOPs)was introduced,and the fundamental theories of MOPSO were described.The MOPSO algorithms were divided into three categories according to their features:Pareto-dominated-based MOPSO,decomposition-based MOPSO,and indicator-based MOPSO,and a detailed description of their existing classical algorithms was also developed.Next,relevant evaluation indicators were described,and seven representative algorithms were selected for performance analysis.The experimental results demonstrated the strengths and weaknesses of each of the traditional MOPSO and three categories of improved MOPSO algorithms.Among them,the indicator-based MOPSO performed better in terms of convergence and diversity.Then,the applications of MOPSO algorithms in production scheduling,image processing,and power systems were briefly introduced.Finally,the limitations and future research directions of the MOPSO algorithm for solving complex optimization problems were discussed.

关 键 词：粒子群优化 多目标优化 PARETO解集 收敛性 多样性 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]