Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies
This work addresses reliability issues in swarm optimization for fields like engineering and healthcare, but it is incremental as it builds on existing PSO methods with a focus on explainability.
The study tackled the problem of low reliability in Particle Swarm Optimization (PSO) due to unclear configurations by analyzing how communication topologies (Ring, Star, Von Neumann) affect convergence and search behaviors, providing practical guidelines for topology selection to enhance transparency and robustness.
Swarm intelligence effectively optimizes complex systems across fields like engineering and healthcare, yet algorithm solutions often suffer from low reliability due to unclear configurations and hyperparameters. This study analyzes Particle Swarm Optimization (PSO), focusing on how different communication topologies Ring, Star, and Von Neumann affect convergence and search behaviors. Using an adapted IOHxplainer , an explainable benchmarking tool, we investigate how these topologies influence information flow, diversity, and convergence speed, clarifying the balance between exploration and exploitation. Through visualization and statistical analysis, the research enhances interpretability of PSO's decisions and provides practical guidelines for choosing suitable topologies for specific optimization tasks. Ultimately, this contributes to making swarm based optimization more transparent, robust, and trustworthy.