Chaos Particle Swarm Optimization Algorithm for Multi-Objective Constrained Optimization Problems
In this paper, an improved particle swarm algorithm based on chaos for multi-objective optimization problems is proposed. The algorithm uses external archive to save the current best solutions, and maintains the archive by density distance, the object dynamic crowding is used to measure Pareto solutions quality and the strategy of eliminating low dynamic crowding solutions is employed to enhance nondominated solutions uniformity. In order to preserve population diversity, the global best is selected randomly from those non-dominated solutions which have bigger density distance in the archive. Personal best position is updated by Pareto dominance relationship. When the basic particle swarm optimization gets into local convergence, chaos disturbance is introduced to guide the swarm to escape from local optima, so it can overcome the defect of traditional particle swarm optimization algorithm on getting into local best and enhance the global exploratory capability of PSO. The proposed algorithm is compared with two well known multiobjective evolutionary algorithms through three standard test functions,the numerical experiment results demonstrate that the obtained Pareto optimal solutions by the algorithm can rapidly converge to the Pareto front and uniformly spread along the front
Multi-objective Constrained optimization chaos particle swarm optimization pareto solutions external archive density distance
Dekun Tan
Department of Computer Science & Technolog Nanchang Institute of Technology Nanchang Jiangxi Province China 330099
国际会议
秦皇岛
英文
432-435
2010-11-05（万方平台首次上网日期，不代表论文的发表时间）