Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis
Dikshit Chauhan, Shivani, P. N. Suganthan
长期以来,大自然一直激励着群体智能(SI)的发展,这是人工智能的一个关键分支,它模拟了在生物系统中观察到的集体行为,以解决复杂的优化问题。 粒子群优化(PSO)因其简单性和效率而被广泛在SI算法中采用。 尽管提出了许多学习策略来提高PSO在收敛速度,稳健性和适应性方面的表现,但这些策略没有全面和系统的分析。 我们审查并分类各种学习策略,以解决这一差距,评估其对优化性能的影响。 此外,还进行了比较实验评估,以检查这些策略如何影响PSO的搜索动态。 最后,我们讨论开放挑战和未来方向,强调需要自我适应的智能PSO变体,能够解决日益复杂的现实世界问题。
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 analys...