基于两阶段领导的多目标粒子群优化算法

Multi-objective PSO optimization algorithm based on two stages-guided

提出一种基于两阶段领导的多目标粒子群算法(P-AMOPSO).该算法包含4个改进策略:基于强支配排序与拥挤距离排序相结合的构造外部集策略,基于两阶段的领导粒子选择策略,基于高斯分布及均匀分布相结合的变异策略,基于邻域认知的个体极值更新策略.通过几个典型的多目标测试函数对P-AMOPSO算法的性能进行测试,并与多目标优化算法进行对比.结果表明,P-AMOPSO算法具有较好的搜索性能.

This paper introduces a multi-objective particle swarm optimization algorithm （P-AMOPSO） based on two stages-guided,which includes four improved strategies,the strategy of constructing external data set based on combining strong predominance ranking and crowding distance ranking,the strategy of selecting guided particle based on two stages,the strategy of mutation based on combining Gaussian distribution mutation and uniform distribution mutation,and the strategy of updating based on personal best of neighborhood consciousness. Some benchmark functions are tested for comparing the performance of P-AMOPSO. The results show the better search performance of P-AMOPSO.