系统梳理粒子群优化学习策略，揭示其对搜索性能的影响。
Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis
- 分类整理多种学习策略，构建系统分析框架
- 实验对比不同策略对收敛速度与鲁棒性的提升效果
- 为自适应智能优化算法设计提供方向参考
自然长期启发群体智能（SI）的发展，这是人工智能的一个重要分支，通过模拟生物系统中的集体行为来解决复杂优化问题。粒子群优化（PSO）因其简单高效而被广泛采用。尽管已有大量学习策略被提出以提升PSO在收敛速度、鲁棒性和适应性方面的表现，但尚未有全面系统的分析。本文对各类学习策略进行回顾与分类，评估其对优化性能的影响。同时，通过对比实验分析这些策略如何改变PSO的搜索动态。最后，讨论开放挑战与未来方向，强调发展具备自适应能力的智能PSO变体以应对日益复杂的现实问题的必要性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。