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.
Abstract (translated)
长期以来，自然界一直启发着群智能（SI）的发展，这是人工智能的一个关键分支，它模仿生物系统中观察到的集体行为来解决复杂的优化问题。粒子群优化（PSO）是众多群智能算法中最广泛采用的一种，因其简单性和高效性而备受青睐。尽管已经提出了许多旨在提高PSO在收敛速度、鲁棒性和适应性方面的性能的学习策略，但尚不存在对这些策略进行全面和系统分析的研究。我们回顾并分类了各种学习策略，以填补这一空白，并评估它们对优化性能的影响。此外，还进行了比较实验评价，以考察这些策略如何影响PSO的搜索动态。最后，我们将讨论开放性的挑战及未来的发展方向，强调需要开发能够应对日益复杂的现实世界问题、具有自适应性和智能性的新型PSO变体的重要性。
URL
https://arxiv.org/abs/2504.11812
