1(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)2(Institute of Modern Educational Technology,Zhejiang University of Technology,Hangzhou 310023,China)3(Institute of Information Intelligence and Decision Optimization,Zhejiang University of Technology,Hangzhou 310023,China)
Abstract：Particle swarm algorithm has a series of advantages such as fast convergence rate and low computation cost.In particular,the speed advantage on the local optimal search is favored by many researchers.Based on the particle swarm algorithm,this paper presents a new grouping strategy which integrates grouping decomposition into the multi-objective particle swarn algorithm to increase the speed of the neighborhood local search.The algorithm presented in this paper performs the best group matching according to the distance from individual to weight vector and the value of each aggregate function.It can obtain Pareto optimal solution set by dynamically usingparticle swarm algorithm to strengthen local search ability.In the simulation experiment,the performance of the proposed algorithm is compared with NSGA-II,MOPSO,MOEA/D and RVEA on the ZDT and DTLZ test functions.The result shows that the proposed algorithm can produce higher quality Pareto optimal solution set which has better convergence and distribution performance than other four algorithms.
2017, Vol. 38 
