Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis

2025-04-16

AI Summary

The lack of systematic analysis and a unified evaluation framework for Particle Swarm Optimization (PSO) learning strategies hinders principled algorithm design. Method: This study establishes the first comprehensive taxonomy of PSO learning strategies and proposes a multidimensional evaluation paradigm tailored to adaptive intelligent variants. Integrating bibliometric analysis, formal strategy modeling, comparative experiments on 20+ benchmark functions, and search-trajectory visualization, it quantitatively characterizes differences across strategies in convergence speed, robustness, and dynamic search behavior. Contribution/Results: Empirical analysis delineates performance boundaries under high-dimensional, multimodal, and noisy conditions, identifying three most promising adaptive mechanisms—feedback-driven regulation, population-diversity-based adaptation, and environment-aware learning. These findings provide an evidence-based foundation and evolutionary roadmap for designing next-generation PSO algorithms with enhanced interpretability and adaptability.

Technology Category

Search and Optimization: Sampling/Simulation-based SearchIntelligent Robots: Learning & Optimization for ROBMachine Learning: Bio-inspired Learning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systemsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Nature has long inspired the development of swarm intelligence (SI), a key branch of artificial intelligence that models collective behaviors observed in biological systems for solving complex optimization problems. Particle swarm optimization (PSO) is widely adopted among SI algorithms due to its simplicity and efficiency. Despite numerous learning strategies proposed to enhance PSO's performance in terms of convergence speed, robustness, and adaptability, no comprehensive and systematic analysis of these strategies exists. We review and classify various learning strategies to address this gap, assessing their impact on optimization performance. Additionally, a comparative experimental evaluation is conducted to examine how these strategies influence PSO's search dynamics. Finally, we discuss open challenges and future directions, emphasizing the need for self-adaptive, intelligent PSO variants capable of addressing increasingly complex real-world problems.

Problem

• Lack of comprehensive analysis of PSO learning strategies
• Need to assess impact of strategies on optimization performance
• Requirement for adaptive PSO variants for complex problems

Innovation

• Review and classify PSO learning strategies
• Assess impact on optimization performance
• Conduct comparative experimental evaluation