Abstract: 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.

Key words: multi-modal multi-objective problem, particle swarm optimization, star topology, distribution uniformity, Pareto set