Particle Swarm Optimization (PSO) frequently suffers from premature convergence. This paper introduces a family of problem-informed diversity-enhancing strategies that manipulate the swarm's social and cognitive components. These include opposing-best strategies that repel particles from optimal regions, negative learning strategies that guide exploration toward poor solutions, and reverse learning strategies that push particles away from inferior regions. These socio-cognitive mechanisms are evaluated against an analogous suite of problem-unaware, explicit randomization strategies that inject randomness either into velocity update components or directly into position updates. The results reveal that the effectiveness of diversity enhancement is determined primarily by how it is embedded within the swarm dynamics, rather than by the mere presence of extraneous problem-informed guidance. Particularly, random perturbations introduced at the velocity-update level consistently outperform those applied directly to particle positions.

翻译：粒子群优化（PSO）常因早熟收敛而受限。本文提出一类基于问题信息的多样性增强策略族，通过调控群智能的社会与认知组件发挥作用，包括：排斥粒子远离最优区域的对抗最优策略、引导搜索趋向劣质解的负学习策略，以及推动粒子远离低劣区域的逆学习策略。这些社会认知机制与一套不含问题信息的显式随机化策略进行对照评估——后者通过向速度更新组件或直接向位置更新组件注入随机性来运作。结果表明，多样性增强的有效性主要取决于其在群动态中的嵌入方式，而非单纯依赖外部问题指导信息的引入。特别地，在速度更新层面引入的随机扰动始终优于直接作用于粒子位置的扰动。