基于人类社交行为的动态多目标优化

计算机科学 ›› 2015, Vol. 42 ›› Issue (8): 249-252.

基于人类社交行为的动态多目标优化

伍大清,郑建国,朱佳俊,孙莉

南华大学计算机科学与技术学院衡阳421001;成都大学模式识别与智能信息处理四川省重点实验室成都610106;东华大学旭日工商管理学院上海200051,东华大学旭日工商管理学院上海200051,江南大学商学院无锡214000,东华大学旭日工商管理学院上海200051

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受湖南省教育厅资助

Dynamic Multi-objective Particle Swarm Optimization Algorithm Based on Human Social Behavior

WU Da-qing, ZHENG Jian-guo, ZHU Jia-jun and SUN Li

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 为了提高多目标微粒群优化算法处理多目标优化问题的性能,降低计算复杂度,改善算法的收敛性,提出了一种基于人类社交行为的多目标动态微粒群优化算法。考虑到粒子寻优过程受到环境中精英粒子与平庸粒子的影响,分别对自身产生推力与阻力作用,并引入局部跳出策略,使算法具有很强的全局搜索能力和较好的鲁棒性能。通过典型的多目标优化函数对算法进行了测试验证,结果表明提出的多目标算法具有较快的收敛速度和较强的跳出局部最优能力,性能优越,可供许多领域优化问题求解借鉴。

关键词: 多目标优化算法,精英粒子,平庸粒子,局部跳出策略

Abstract: In order to improve the processing performance of the multi-objective optimization problem,reduce the computational complexity and improve the convergence of the algorithm,a multi-objective particle swarm optimization algorithm based on a human social behavior was proposed.The strategies such as promotion/resistance factor and the local jump strategy are introduced in proposed algorithm,to make the algorithm have strong global search ability and good robust performance.Some typical multi-objective optimization functions were tested to verify the algorithm.The results show that the proposed algorithm has superior performance of fast convergence speed and strong ability to jump out of local optimum,so it can be used for many fields.

Key words: Multi-objective optimization algorithm,Elite particle,Mediocrity particle,Local jump strategy

伍大清,郑建国,朱佳俊,孙莉. 基于人类社交行为的动态多目标优化[J]. 计算机科学, 2015, 42(8): 249-252. https://doi.org/

WU Da-qing, ZHENG Jian-guo, ZHU Jia-jun and SUN Li. Dynamic Multi-objective Particle Swarm Optimization Algorithm Based on Human Social Behavior[J]. Computer Science, 2015, 42(8): 249-252. https://doi.org/

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