解决多目标优化问题的差分进化算法研究进展

摘要

Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm.It has drawn the attention of researchers all round the globe with its perfect performance since its inception.As a global search of metaheuristics based on population,DE has many successful scientific and engineering applications.A survey of DE for solving multi-objective optimization problems (MOPs) is presented.A detailed review of the basic concepts of DE is provided.Several important variants of DE for solving MOPs are presented.Moreover,the theoretical analyses on DE for solving MOPs are provided.Finally,the engineering applications of DE for solving MOPs and its future challenging field are also pointed out in the remainder of this paper.%差分进化(differential evolution,DE)是一种简单但功能强大的进化优化算法.由于其优秀的性能,其诞生之日起就吸引了各国研究人员的关注.作为一种基于群体的全局性启发式搜索算法,差分进化算法在科学和工程中有许多成功的应用.本文对解决多目标优化问题的差分进化算法研究进行了综述,对差分进化的基本概念进行了详细的描述,给出了几种解决多目标优化问题的差分进化算法变体,并且给出了差分进化算法解决多目标优化问题的理论分析,最后,给出了差分进化算法解决多目标优化问题的工程应用,并指出了未来具有挑战性的研究领域.