Swarm based optimization algorithms have demonstrated remarkable success in solving complex optimization problems. However, their widespread adoption remains sceptical due to limited transparency in how different algorithmic components influence the overall performance of the algorithm. This work presents a multi-faceted interpretability related investigations of Particle Swarm Optimization (PSO). Through this work, we provide a framework that makes the PSO interpretable and explainable using novel machine learning approach. We first developed a comprehensive landscape characterization framework using Exploratory Landscape Analysis to quantify problem difficulty and identify critical features in the problem that affects the optimization performance of PSO. Secondly, we develop an explainable benchmarking framework for PSO. The work successfully decodes how swarm topologies affect information flow, diversity, and convergence. Through systematic experimentation across 24 benchmark functions in multiple dimensions, we establish practical guidelines for topology selection and parameter configuration. A systematic design of decision tree is developed to identify the decision making inside PSO. These findings uncover the black-box nature of PSO, providing more transparency and interpretability to swarm intelligence systems. The source code is available at https://github.com/GitNitin02/ioh_pso.

翻译：基于群体的优化算法在解决复杂优化问题方面取得了显著成功。然而，由于不同算法组件对整体性能的影响机制缺乏透明度，其广泛应用仍存在疑虑。本文针对粒子群优化算法（PSO）开展了多方面可解释性研究，提出了一种基于新型机器学习方法实现PSO可解释与可解释性的框架。我们首先利用探索性景观分析构建了全面的景观特征框架，用以量化问题难度并识别影响PSO优化性能的关键问题特征。其次，开发了PSO可解释基准测试框架。本工作成功解码了种群拓扑结构如何影响信息流动、多样性和收敛性。通过在24个基准函数的多维度系统实验，我们建立了拓扑选择与参数配置的实践指南。系统设计的决策树可识别PSO内部决策机制。这些发现揭示了PSO的黑箱特性，为群体智能系统提供了更高的透明度与可解释性。源代码获取地址：https://github.com/GitNitin02/ioh_pso