Aiming at the multimodal characteristics of multi-objective optimization problems, which need to provide decision-makers with a variety of choices or various information, this paper proposes a multi-modal multi-objective particle swarm optimization algorithm using ring topology and neighborhood disturbance strategy. Using the system topology, the population can form multiple independent search environments without specifying any parameters. To better global exploration and local development, an automatic conversion mechanism of global search and local search is proposed. The stagnation detection strategy is introduced to disturb the neighborhood optimal particles to improve the diversity of particle swarm optimization. Prevent the algorithm from premature convergence to a pareot optimal solution set. Experimental analysis verifies the feasibility of the proposed algorithm. Experiments are carried out with the comparison method. The experimental results show that the proposed algorithm can better ensure the diversity and convergence in the target space, which is to find more complete and evenly distributed optimal solution sets.
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