进化多目标优化算法研究  

Research on Evolutionary Multi-Objective Optimization Algorithms

作　　者：公茂果[1,2] 焦李成[1,2] 杨咚咚[1,2] 马文萍[1,2] 

机构地区：[1]西安电子科技大学智能信息处理研究所,陕西西安710071 [2]西安电子科技大学智能感知与图像理解教育部重点实验室,陕西西安710071

出　　处：《软件学报》2009年第2期271-289,共19页Journal of Software

基　　金：国家自然科学基金;国家高技术研究发展计划(863);国家重点基础研究发展计划(973);长江学者和创新团队支持计划~~

摘　　要：进化多目标优化主要研究如何利用进化计算方法求解多目标优化问题,已经成为进化计算领域的研究热点之一.在简要总结2003年以前的主要算法后,着重对进化多目标优化的最新进展进行了详细讨论.归纳出当前多目标优化的研究趋势,一方面,粒子群优化、人工免疫系统、分布估计算法等越来越多的进化范例被引入多目标优化领域,一些新颖的受自然系统启发的多目标优化算法相继提出;另一方面,为了更有效的求解高维多目标优化问题,一些区别于传统Pareto占优的新型占优机制相继涌现;同时,对多目标优化问题本身性质的研究也在逐步深入.对公认的代表性算法进行了实验对比.最后,对进化多目标优化的进一步发展提出了自己的看法.Evolutionary multi-objective optimization （EMO）, whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.

关 键 词：多目标优化 进化算法 PARETO占优 粒子群优化 人工免疫系统 分布估计算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]