星型结构的多目标粒子群算法求解多模态多目标问题

首先,根据多目标粒子群算法中的粒子结构信息,利用非支配解集构造粒子个体邻域之间的拓扑结构,提出星型结构的多目标粒子群算法用于求解多模态多目标问题。其次,针对多目标粒子群中全局最优个体选择困难,提出一种非支配解集分布均匀程度的评价方法,评价结果用于确定当前粒子对应的全局最优个体。最后,结合2种方法提出带均匀计算方法的星型拓扑结构多目标粒子群优化算法STMOPSONCMIU。通过测试函数分析算法的收敛性,表明改进的算法比原来的算法收敛速度快。实验结果表明,该算法可以较好地兼顾问题的目标空间和决策空间的分布,有效解决多模态多目标问题。

A multi-objective particle swarm optimization algorithm with star structure to solve the multi-modal multi-objective problem

Firstly, according to the particle structure information in the multi-objective particle swarm optimization algorithm, using non-dominated solution sets to construct the topological structure between individual particle neighborhoods, a star-structured multi-objective particle swarm optimization algorithm is proposed for solving multi-modal multi-objective problems. Secondly, in view of the difficulty of selecting the global optimal individual in the multi-objective particle swarm, an evaluation method for the uniformity of the distribution of non-dominated solution sets is proposed. The evaluation result determines the global optimal individual corresponding to the current particle. Finally, combining two methods, a star topology multi-objective particle swarm optimization algorithm with uniform calculation method is proposed. The test function analyzes the convergence of the algorithm and shows that the improved algorithm converges faster than the original algorithm. Experimental results show that the algorithm can take into account the distribution of the problem object space and decision space, and effectively solve the multi-modal multi-objective problem.