Abstract
Abstract:
In this paper, a new multi-swarm method is proposed for multiobjective
particle swarm optimization. To enhance the Pareto front searching
ability of PSO, the particles are divided into many swarms. Several swarms are
dynamically searching the objective space around some points of the Pareto
front set. The rest of particles are searching the space keeping away from the
Pareto front to improve the global search ability. Simulation results and
comparisons with existing Multi-objective Particle Swarm Optimization
methods demonstrate that the proposed method effectively enhances the search
efficiency and improves the search quality.
Originally presented at 2011 International Conference on Intelligent Computing, Zhengzhou, China 11-14 August, 2011.