Learning strategies for particle swarm optimizer: A critical review and performance analysis

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. Our analysis reveals that multi-swarm strategies consistently outperform other PSO strategies in high-dimensional and multimodal problems, offering better exploration and convergence trade-offs. 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. This survey not only synthesizes the current landscape of learning-enhanced PSO but also provides actionable insights for future research and algorithmic design.