🤖 AI Summary
Particle swarm optimization (PSO) often suffers from premature convergence due to insufficient population diversity. This study systematically investigates both problem-aware and problem-agnostic diversity-enhancing mechanisms, introducing novel social cognition strategies—namely opposition to the best, negative learning, and opposition-based learning—and incorporates guided and random perturbations into the velocity and position update procedures. The findings reveal that embedding random perturbations within the velocity update significantly outperforms direct perturbation of particle positions. More importantly, the manner in which diversity mechanisms are integrated into the algorithmic framework exerts a far greater influence on performance than whether or not problem-specific information is utilized, thereby underscoring the critical role of structural design in enhancing global search capability.
📝 Abstract
Particle Swarm Optimization (PSO) frequently suffers from premature convergence. This paper introduces a family of problem-informed diversity-enhancing strategies that manipulate the swarm's social and cognitive components. These include opposing-best strategies that repel particles from optimal regions, negative learning strategies that guide exploration toward poor solutions, and reverse learning strategies that push particles away from inferior regions. These socio-cognitive mechanisms are evaluated against an analogous suite of problem-unaware, explicit randomization strategies that inject randomness either into velocity update components or directly into position updates. The results reveal that the effectiveness of diversity enhancement is determined primarily by how it is embedded within the swarm dynamics, rather than by the mere presence of extraneous problem-informed guidance. Particularly, random perturbations introduced at the velocity-update level consistently outperform those applied directly to particle positions.