【摘要】 为了提升粒子群算法求解多目标问题的能力,通过分析初始种群的方法对算法的影响,提出一种基于正交设计的多目标粒子群算法(ODMOPSO)。在算法运行过程中,通过正交设计来产生初始种群,使得种群均匀分布在可行区域,进而使得算法能够在整个可行解空间上进行均匀搜索;同时,引入广义学习策略提升粒子向Pareto前沿飞行的概率。在基准函数的测试中,结果显示ODMOPSO算法获得了质量更高的解。更多还原

【Abstract】 In order to improve the ability of PSO in solving multi-objective optmization, by discussing the relationship between method of initialization swarm and algorithm performance,this paper proposed a multi-objective PSO based on orthogonal design (ODMOPSO for short). In the proposed algorithm,firstly,used orthogonal design to initialize the population,which made the algorithm can search in the whole feasible space;next,introduced the comprehensive learning strategy to improve the probability of flying to Pareto front. In benchmark functions, simulation results show that the ODMOPSO algorithm can find better solutions.更多还原