Particle Swarm Optimization with Population Dynamics - Artificial Splitting, Extinction, and Migration

Abstract

Many Particle Swarm Optimization (PSO) variants often fail to approach the global optimum due to complex fitness landscapes and/or the structural rigidity of these variants. We developed a novel framework named PSO-SEM, centered on macro-evolutionary population management. PSO-SEM introduces three landscape-driven operators: Split (Topological Fission), Extinction (Density-driven Recycling), and Migration (Knowledge Transfer). These operators autonomously regulate the lifecycle of sub-swarms to intensify search in high-potential areas by identifying promising basins and recycling computational resources from stagnant regions. Experimental results show that PSO-SEM achieves a top-tier ranking and demonstrates significant competitiveness against 14 state-of-the-art algorithms. Behavioral monitoring and ablation studies confirm PSO-SEM's ability to maintain autonomous exploration/exploitation balance through landscape-based computational resource re-allocation. Our findings verify that PSO-SEM is an interpretable architecture that meets the diverse requirements of black-box optimization.