基于Pareto最优解集的多目标粒子群优化算法

A Multi-Objective Particle Swarm Algorithm Based on the Pareto Optimization Solution Set

本文结合Pareto支配思想、精英保留策略、锦标赛和排挤距离选择技术,对传统的粒子更新策略进行改进,给出了一种新的粒子淘汰准则,提出了一种基于Pareto最优解集的多目标粒子群优化算法。最后,通过7个多目标标准测试函数进行测试。测试结果表明,该方法有效可行,其性能优于如NSGAII、SPEA2等多目标优化算法。

This paper presents a novel effective multi-objective particle swarm algorithm based on the Pareto non-dominated set,in which the Pareto non-dominated ranking, the elitism strategy, the tournament selection and the crowding dis- tance method are integrated into a new rule by improving the update strategy of particles. Finally, seven classical unctions are used to test the performance of the algorithm. Experimental results show that the proposed approach is efficient and outperforms the conventional algorithms such as NSGAI1,SPEA2.