Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies

2025-04-17

AI Summary

PSO suffers from unpredictable convergence behavior and low decision credibility due to the lack of theoretical guidance in selecting hyperparameters and communication topologies. This paper systematically quantifies the impact of three canonical topologies—ring, star, and von Neumann—on PSO’s convergence properties, information propagation efficiency, and exploration–exploitation trade-off. We introduce the first explainability framework for PSO, IOHxplainer, integrating dynamic visualization, statistical hypothesis testing, and controlled experiments across multiple topology-specific PSO variants. Our analysis uncovers fundamental differences in population diversity evolution and convergence trajectories induced by each topology, yielding empirically grounded guidelines for topology selection. The results substantially enhance PSO’s behavioral transparency, task-specific adaptability, and optimization robustness, thereby providing a methodological foundation for trustworthy deployment of swarm intelligence algorithms.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Enhancing PSO explainability through communication topologies analysis
• Investigating topology effects on convergence and search behaviors
• Providing guidelines for reliable topology selection in optimization

Innovation

• Analyzing PSO with Ring, Star, Von Neumann topologies
• Using IOHxplainer for explainable benchmarking
• Visualizing topology effects on exploration and exploitation