关键词: 多目标优化, 粒子群算法, 交叉, 变异, 外部存档

Abstract: In order to minimize the distance of the Pareto front produced by PSO with respect to the global Pareto front and maximize the spread of solutions found by PSO, we proposed an multi-objective particle swarm optimizer based on crossover and mutation (CMMOPSO for short). In the CMMOPSO algorithm, firstly, the number of particle in sparse part of Pareto front is defined and the crossover operator is employed to increase the diversity of the nondominated solutions; next, the mutation operation is used for the particles far away from Pareto front to improve the probability to fly to Pareto front. In benchmark functions, CMMOPSO achieves better solutions than other algorithms. Consequently, CMMOPSO can be used as an effective algorithm to solve multi-objective problems.

Key words: Multi-objective optimization, Particle swarm optimizer, Crossover, Mutation, External archive