Adaptive Particle Swarm Optimization
Adaptive Particle Swarm Optimization
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DOI：
10.1109/tsmcb.2009.2015956
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发表时间：
2009-12-01
影响因子：
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通讯作者：
Chung, Henry Shu-Hung
中科院分区：
文献类型：
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作者：
Zhan, Zhi-Hui;Zhang, Jun;Chung, Henry Shu-Hung
提出了比经典粒子群优化（PSO）具有更好搜索效率的自适应粒子群优化（APSO）。更重要的是，它可以以更快的收敛速度对整个搜索空间进行全局搜索。 APSO由两个主要步骤组成。首先，通过评估种群分布和粒子适应性，执行实时进化状态估计程序，以识别以下四个定义的进化状态之一，包括勘探，剥削，收敛性和在每一代中跳出来。它可以在运行时自动控制惯性重量，加速系数和其他算法参数，以提高搜索效率和收敛速度。然后，当将进化状态归类为融合状态时，将执行精英学习策略。该策略将在全球最佳粒子上行动，以跳出可能的本地最佳选择。 APSO已在12个单峰和多模式基准函数上进行了全面评估。将研究参数适应和精英学习的影响。结果表明，APSO在收敛速度，全球最优性，解决方案准确性和算法可靠性方面显着提高了PSO范式的性能。由于APSO仅向PSO范式介绍了两个新参数，因此它不会引入其他设计或实现复杂性。
An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity.