Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis
Dikshit Chauhan, Shivani, P. N. Suganthan
TL;DR
This paper surveys learning strategies for particle swarm optimization (PSO), proposing a taxonomy that spans single-, two-, and multi-swarm approaches to enhance exploration, robustness, and convergence. It analyzes fundamental mechanisms like CLPSO, OLPSO, DLS, and many hybrids, and provides a comprehensive experimental comparison across diverse benchmarks to reveal when certain strategies excel. Key contributions include a structured classification, a multi-faceted experimental validation, and a discussion of open challenges such as adaptive strategy selection and explainability. The findings highlight that no universal best strategy exists; instead, performance hinges on problem structure and dimensionality, motivating self-adaptive, intelligent PSO variants for real-world optimization.
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.