自适应分解式多目标粒子群优化算法

为了提高多目标粒子群优化算法解的分布性,文中提出了一种自适应分解式多目标粒子群优化算法(Adaptive Multiobjective Particle Swarm Optimization based on Decomposed Archive,AMOPSO-DA).首先,设计了一种基于优化解空间分布信息的外部档案更新策略,有效提升了AMOPSO-DA的空间搜索能力;其次,提出了一种基于粒子进化方向信息的飞行参数调整方法,有效平衡了AMOPSO-DA的探索和开发能力.最后,将提出的AMOPSO-DA应用于多目标优化问题,实验结果表明,文中提出的AMOPSO-DA能够获得分布性较好的优化解.

Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive

To improve the distribution performance of multiobjective particle swarm optimization algorithm,an adaptive multiobjective particle swarm optimization algorithm,based on the decomposed archive,named AMOPSO-DA,is developed in this paper.First,an external archive update strategy,based on the spatial distribution information of optimal solutions,is designed to improve the searching ability of AMOPSO-DA.Second,an adaptive flying parameter adjustment strategy,based on the evolutionary direction information of each particle,is proposed to balance the exploration ability and the exploitation ability.Finally,this proposed AMOPSO-DA is applied to some multiobjective optimization problems.The experiment results demonstrate that AMOPSO-DA can obtain well-distributed optimal solutions.