Abstract

Social Learning Particle Swarm Optimization (SLPSO) is an advanced variant of the PSO algorithm, designed to enhance optimization performance in Large-Scale Global Optimization (LSGO) problems. However, SLPSO encounters significant challenges, particularly in maintaining a balanced trade-off between exploration and exploitation, which limits its effec­ tiveness in complex optimization tasks. In response to these limitations, this paper proposes the Pulse-based Collective Learning Swarm Optimizer (PCLSO).

社会学习粒子群优化（SLPSO）是为增强大规模全局优化性能而设计的先进 PSO 变体。本文提出脉冲策略集体学习群优化器，通过引入脉冲策略进一步改善大规模全局优化问题的求解性能。

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