Auto-Topology Particle Swarm Optimization for Multimodal Multiobjective Problems
提出一种自动拓扑粒子群优化方法，通过动态信息传输拓扑高效发现多模态多目标问题中多个等效帕累托最优解集，实验表明性能优于现有算法。
Multimodal multiobjective problems (MMOPs) are common in engineering and are characterized by multiple equivalent Pareto optimal sets. However, the asymmetric distribution of these sets makes it challenging to discover diverse solutions efficiently. To tackle this issue, an auto-topology particle swarm optimization (ATPSO) method is proposed for MMOPs, forming a dynamic search paradigm via a flexible information transmission topology. First, the multidimensional topology structure is proposed to construct a parallel information transmission topology. Then, the particles are partitioned for locally variable information transmission. Second, the topology performance indicators are designed to measure search disparities of multiple Pareto optimal sets. Then, the indicator growth between decision and objective spaces is compared to measure search disparities mapped in the multidimensional topologies. Third, a parallel optimization strategy for the topology structure is proposed to dynamically regulate the multidimensional topology structure. Then, the appropriate topology structure is automatically formed based on the indicator-oriented structure optimization. Finally, the experimental results illustrate that ATPSO has superior performance over state-of-the-art algorithms in addressing MMOPs.