Explainable Information Processing in Particle Swarm Optimization through Landscape and Search Behavior Analysis
Read the original on arXiv Machine Learning →The paper introduces a dual‑perspective explainability framework for Particle Swarm Optimization (PSO). From a landscape viewpoint, it uses Exploratory Landscape Analysis (ELA) and machine‑learning classifiers to predict topology‑specific hyperparameters for unseen problems. From an algorithmic viewpoint, it incorporates IOHxplainer and Search Trajectory Networks (STN) with new metrics—Connectivity Density, Fragmentation Score, and Search Efficiency—to visualize and quantify PSO’s search organization and transition effectiveness across 24 benchmark functions and multiple topologies.
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