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Confident but weakly informed: Tackling PSO's momentum conundrum

机译:有信心,但消息不灵通:解决PSO的动力难题

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Particle Swarm Optimization uses noisy historical information to select potentially optimal function samples. Though information-theoretic principles suggest that less noise indicates greater certainty, PSO's momentum term is usually both the least informed and the most deterministic. This dichotomy suggests that while momentum has a profound impact on swarm diversity, it would benefit from a more principled approach. We demonstrate that momentum can be made both more effective and better behaved with informed feedback, and that it may even be completely eliminated with proper application of more straightforward and well-behaved diversity injection strategies.
机译:粒子群优化使用嘈杂的历史信息来选择潜在的最佳函数样本。尽管信息理论原理表明,噪声越小表示确定性越高,但是PSO的动量项通常既是信息最少的,也是确定性最高的。这种二分法表明,尽管动量对群体多样性具有深远的影响,但它会受益于更原则性的方法。我们证明,在知情反馈的情况下,动量既可以变得更有效,又可以表现得更好,并且通过适当应用更简单,行为良好的多样性注入策略,甚至可以完全消除动量。

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