1.Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044 2.School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044
Abstract：Considerating the importance of balancing global and local search for multi-objective particle swarm optimization algorithm(MOPSO) to obtain the complete and uniform Pareto front(PF), a balance search strategy is designed and an improved multi-objective particle swarm optimization algorithm (bsMOPSO) is proposed.The strategy is composed of two novel search sub-strategies. In the local search sub-strategy, self-exploitation of archive set is designed to achieve local search involving the entire Pareto front by disturbing fixed ratio of uniform particles in archive set with Cauchy mutation. In the global search sub-strategy, guided search by the best boundary particle is designed through using the optimal boundary particle as the global optimal solution, and therefore more boundary areas of each objective function are searched by part of particle swarm. By comparing five algorithms on the series of ZDT and DTLZ test functions, the results demonstrate that bsMOPSO achieves better Pareto optimal convergence and distribution.
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2017, Vol. 30 
