Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.

中文翻译：

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用于粒子群优化的惯性权重控制策略

粒子群优化（PSO）是一种基于种群的随机优化技术，受到鸟类社会动态的启发。PSO算法对控制参数相当敏感，因此，已经有大量的研究工作致力于动态调整这些参数。自适应方法的重点主要围绕调整惯性权重，因为它与PSO算法的勘探/开发平衡之间表现出最明显的关系。但是，尽管进行了大量的研究工作，但许多惯性权重控制策略尚未得到分析或经验的彻底检验。因此，在选择惯性重量控制策略时有很多选择，但是还没有足够全面的研究来确定性地指导选择。本文首先通过概述18种惯性重量控制策略来解决这些问题。其次，推导了策略表现出收敛行为所需的条件。最后，对一系列60个基准问题进行了惯性权重控制策略的经验检验。实证研究的结果表明，除了随机选择的惯性权重之外，没有任何检查过的策略甚至可以与恒定惯性权重相提并论。