A Machine Learning Based Explainability Framework for Interpreting Swarm Intelligence
arXiv:2509.06272v4 Announce Type: replace Abstract: 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 p
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Abstract: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 this https URL.
Comments: Upated: 31-03-26
Subjects:
Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
Cite as: arXiv:2509.06272 [cs.NE]
(or arXiv:2509.06272v4 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2509.06272
arXiv-issued DOI via DataCite
Submission history
From: Anupam Yadav [view email] [v1] Mon, 8 Sep 2025 01:39:32 UTC (13,711 KB) [v2] Wed, 29 Oct 2025 07:12:09 UTC (16,369 KB) [v3] Mon, 10 Nov 2025 09:19:57 UTC (16,364 KB) [v4] Tue, 31 Mar 2026 07:38:35 UTC (13,710 KB)
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