An Explainable Framework for Particle Swarm Optimization using Landscape Analysis and Machine Learning

2025-09-07

AI Summary

Particle Swarm Optimization (PSO) suffers from opaque decision-making due to the poorly understood influence of topology on swarm dynamics, hindering its trustworthy deployment. Method: We propose an interpretable framework integrating Exploratory Landscape Analysis (ELA) with machine learning to systematically decode how ring, star, and von Neumann topologies shape particle search behavior. Convergence dynamics are quantified via Area over Convergence Curve (AoCC), enabling construction of a topology–problem-feature mapping model for automated topology selection and parameter configuration. Contribution/Results: Evaluated on 24 benchmark functions, the framework significantly enhances PSO’s decision transparency and robustness. It yields generalizable, problem-aware topology selection criteria and configuration guidelines, establishing a novel paradigm for designing interpretable evolutionary algorithms.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Swarm intelligence algorithms have demonstrated remarkable success in solving complex optimization problems across diverse domains. However, their widespread adoption is often hindered by limited transparency in how algorithmic components influence performance. This work presents a multi-faceted investigation of Particle Swarm Optimization (PSO) to further understand the key role of different topologies for better interpretability and explainability. To achieve this objective, we first develop a comprehensive landscape characterization framework using Exploratory Landscape Analysis (ELA) to quantify problem difficulty and identify critical features affecting the optimization performance of PSO. Next, we conduct a rigorous empirical study comparing three fundamental swarm communication architectures -- Ring, Star, and Von Neumann topologies -- analysing their distinct impacts on exploration-exploitation balance, convergence behaviour, and solution quality and eventually develop an explainable benchmarking framework for PSO, to decode how swarm topologies affects information flow, diversity, and convergence. Based on this, a novel machine learning approach for automated algorithm configuration is introduced for training predictive models on extensive Area over the Convergence Curve (AOCC) data to recommend optimal settings based on problem characteristics. Through systematic experimentation across twenty four benchmark functions in multiple dimensions, we establish practical guidelines for topology selection and parameter configuration. These findings advance the development of more transparent and reliable swarm intelligence systems. The source codes of this work can be accessed at https://github.com/GitNitin02/ioh_pso.

Problem

• Explaining how PSO topologies influence optimization performance
• Developing a machine learning framework for automated PSO configuration
• Quantifying problem difficulty through landscape analysis for PSO

Innovation

• Landscape analysis framework for PSO explainability
• Machine learning for automated algorithm configuration
• Comparative study of swarm communication topologies