As an effective evolutionary algorithm, particle swarm optimization (PSO) has been widely used to solve single or multi-objective optimization problems. However, the performance of PSO in solving multi-objective problems is unsatisfactory, so a variety of PSO has been proposed to enhance the performance of PSO on multi-objective optimization problems. In this paper, a modified particle swarm optimization (AMPSO) is proposed to solve the multimodal multi-objective problems. Firstly, a dynamic neighborhood-based learning strategy is introduced to replace the global learning strategy, which enhances the diversity of the population. Meanwhile, to enhance the performance of PSO, the offering competition mechanism is utilized. 11 multimodal multi-objective optimization functions are utilized to verify the feasibility and effectiveness of the proposed AMPSO. Experimental results and statistical analysis indicate that AMPSO has competitive performance compared with 5 state-of-the-art multimodal multi-objective algorithms.

作为一种有效的进化算法，粒子群优化（PSO）已被广泛用于解决单目标或多目标优化问题。但是，PSO在解决多目标问题上的性能并不理想，因此提出了多种PSO来提高PSO在多目标优化问题上的性能。为了解决多峰多目标问题，提出了一种改进的粒子群算法（AMPSO）。首先，引入了一种动态的基于邻域的学习策略来代替全球学习策略，这增强了人口的多样性。同时，为了提高PSO的性能，利用了报价竞争机制。利用11个多模式多目标优化函数来验证所提出的AMPSO的可行性和有效性。实验结果和统计分析表明，与5种最新的多模式多目标算法相比，AMPSO具有竞争优势。