AI RESEARCH
A Machine Learning Based Explainability Framework for Interpreting Swarm Intelligence
arXiv CS.LG
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ArXi:2509.06272v4 Announce Type: replace-cross Swarm based optimization algorithms have nstrated 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.