Particle swarm optimizer simulation research of multi-objective optimization problems
多目标优化问题的粒子群算法仿真研究*

This paper proposed a kind of particle swarm optimizer used in solving multi-objective optimization problem (CMMOPSO for short). In the CMMOPSO algorithm, used the external archive to store the non-dominated solutions at each iteration and adopted the crowding distance to keep the size of the external archive. Proposed a kind of novel strategy to select the global best particle(based on crowding distance and convergence distance) to improve probability of flying to Pareto front. At last, to improve the ability to escape from local optima, performed the mutation operation by the certain mutation probability. Conducted experiments on a set of classical benchmark functions. Simulation results show that the proposed algorithm good performance. Consequently, CMMOPSO can be used as an effective algorithm to solve multi-objective problems.