Adaptive Particle Swarm Optimization
Adaptive Particle Swarm Optimization
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DOI：
10.1007/978-3-540-87527-7_21
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发表时间：
2008-09
期刊：
影响因子：
--
通讯作者：
Zhi-hui Zhan;Jun Zhang
中科院分区：
文献类型：
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作者：
Zhi-hui Zhan;Jun Zhang
提出了一种基于进化状态估计（ESE）的自适应粒子群优化算法（APSO）。ESE方法利用每代种群的分布信息和相对粒子适应度信息，构造一个“进化因子”，通过模糊分类方法估计进化状态。根据辨识的状态，考虑到算法控制参数的各种影响，为加快收敛速度，对惯性权重和加速度系数提出了自适应控制策略。此外，一个自适应的“精英学习策略”（ELS）的设计最好的粒子跳出可能的局部最优和/或改善其准确性，从而大大提高了整体解决方案的质量。在6个单峰和多峰函数上对APSO算法进行了测试，实验结果表明，APSO算法在求解精度、收敛速度和算法可靠性等方面均优于PSO算法。
This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an ‘evolutionary factor’ by using the population distribution information and relative particle fitness information in each generation, and estimates the evolutionary state through a fuzzy classification method. According to the identified state and taking into account various effects of the algorithm-controlling parameters, adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. Further, an adaptive ‘elitist learning strategy’ (ELS) is designed for the best particle to jump out of possible local optima and/or to refine its accuracy, resulting in substantially improved quality of global solutions. The APSO algorithm is tested on 6 unimodal and multimodal functions, and the experimental results demonstrate that the APSO generally outperforms the compared PSOs, in terms of solution accuracy, convergence speed and algorithm reliability.