A Multi-objective Particle Swarm Optimization Based on P System Theory
Particle swarm optimization(PSO)algorithm has been widely applied in solving multi-objective optimization problems(MOPs)since it was proposed.However,PSO algorithms updated the velocity of each particle using a single search strategy,which may be difficult to obtain approximate Pareto front for complex MOPs.In this paper,inspired by the theory of P system,a multi-objective particle swarm optimization(PSO)algorithm based on the framework of membrane system(PMOPSO)is proposed to solve MOPs.According to the hierarchical structure,objects and rules of P system,the PSO approach is used in elementary membranes to execute multiple search strategy.And non-dominated sorting and crowding distance is used in skin membrane for improving speed of convergence and maintaining population diversity by evolutionary rules.Compared with other multi-objective optimization algorithm including MOPSO,dMOPSO,SMPSO,MMOPSO,MOEA/D,SPEA2,PESA2,NSGAII on a benchmark series function,the experimental results indicate that the proposed algorithm is not only feasible and effective but also have a better convergence to true Pareto front.
Taowei Chen Yiming Yu Kun Zhao
Information School,Yunnan University of Finance and Economics,650221,Kunming China Information Center,Yunnan University of Finance and Economics,650221,Kunming China
国际会议
上海
英文
1-7
2018-10-12（万方平台首次上网日期，不代表论文的发表时间）