Jul 2026· Annual Conference on Genetic and Evolutionary Computation· pp. 1295-1303· 0 citations· 38 references
Computer Science
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.
Particle Swarm Optimization (PSO) remains highly effective on many continuous optimization problems, yet its search dynamics collapse too quickly on high-dimensional and deceptive landscapes. Our earlier work introduced single-role diversity-enhancing PSO variants and showed that diversity injection is effective only w...
Piotr Urbańczyk, Aleksandra Urbanczyk, Leszek Rutkowski et al.· Vietnam Journal of Computer...· 0 citations
Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-...
Yao-Pei Wang, Yufeng Wang, Ke Liu· Algorithms· 0 citations
Adaptive Hybrid PSO (AHPSO), which uses a sigmoid function on swarm diversity to automatically modulate gradient influence: near-zero during exploration, near-maximum during exploitation, with no manual phase-switching, is proposed.
Particle swarm optimization (PSO) is a widely used bio-inspired optimization method. However, global guidance can align particle trajectories, reduce population diversity, and lead to premature convergence. To address this issue, we propose GRACE-PSO, a particle swarm optimizer based on group rank assessment and cooper...
The proposed experimental framework provides a methodological basis for the experimental characterization of emergent behavior in swarm metaheuristics and establishes relative diversity loss as a homogeneous criterion for defining equivalent comparison states across different search processes.
The numerical and engineering test results demonstrate that CMSCSO provides high optimization accuracy, strong stability, and good adaptability to complex engineering problems, and can serve as an effective solution method for optimization tasks in structural design, mechanical engineering, and other related fields.
