一种多样性引导的两阶段多目标微粒群算法

A diversity-guided two stages multi-objective particle swarm optimizer

针对现有多目标微粒群算法存在容易陷于局部极值、收敛速度慢、函数评价次数多等不足,提出了一种多样性引导的2阶段多目标微粒群算法,依据种群多样性动态使用不同的变异方式,采用了2种不同的领导微粒选择方式,基于Pareto占优排序和拥挤距离来控制外部档案中解的数目。针对多个多目标测试函数进行了实验,并与其他文献的方法进行了比较,验证了算法的有效性。

Multi-objective particle swarm optimizers are often trapped in local optima, converge slowly and cost more function evaluations. Therefore, a diversity-guided two-stage MOPSO （DTSPSO） was proposed. DTSPSO dynamically selects different mutation operators according to current population diversity and divides into two stages according to its ways of selecting leaders. In addition, Pareto dominance ranking and crowding distance were used to fix the size of the external archive. Experiments were carried out on several classical benchmark functions for multi-objective optimization problems and the results show that DTSPSO is effective in solving various multi-objective optimization problems.