通过通信拓扑优化粒子群算法，提升搜索可靠性和可解释性。
Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies
- 对比环形、星形、冯·诺依曼三种拓扑对信息流动的影响。
- 揭示不同拓扑下探索与利用的平衡关系，影响收敛速度。
- 为特定任务选择合适拓扑提供可操作指导，适合优化研究者。
群体智能在工程、医疗等领域有效优化复杂系统，但算法结果常因配置和超参数不明确而可靠性低。本研究聚焦粒子群优化（PSO），分析环形、星形、冯·诺依曼三种通信拓扑对收敛性与搜索行为的影响。借助改进的IOHxplainer——一个可解释性基准工具，探究这些拓扑如何影响信息流、种群多样性及收敛速度，阐明探索与利用之间的权衡。通过可视化与统计分析，增强对PSO决策过程的理解，为特定优化任务选择合适拓扑提供实践指南。最终推动基于群体的优化方法更透明、鲁棒且可信。
原文摘要 · Abstract (English)
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.
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