Particle Swarm Optimization Based on Punctuated-equilibrium Model

Zhenzhou An, Jun Zhang, Yang Yang, Xiaoyan Wang
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Abstract

This paper presents a modified particle swarm optimization, named as punctuated-equilibrium particle swarm optimization (PEPSO). This method refers to punctuated-equilibrium Model (PEM) which is a pattern of group development in organizational behavior. PEM uses the long-term equilibrium phase and the short-term abrupt phase to solve the problem of group stagnation. The present work mathematically modelled this alternating process. The efficiency of the proposed PEPSO was evaluated using CEC 2014 benchmark functions. The experiments showed that PEPSO could solve premature convergence and had more good convergence accuracy than PSO on some test functions. Furthermore, it was also confirmed that the swarm was divided into different groups and the individuals of every group almost acted in unison. This provides a good explanation between PSO and organizational behavior from a new experimental perspective.
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基于点状平衡模型的粒子群优化
本文提出了一种改进的粒子群优化算法,称为点阵平衡粒子群优化算法(PEPSO)。该方法指的是组织行为学中群体发展的一种模式——间断均衡模型(PEM)。PEM采用长期平衡阶段和短期突变阶段来解决群体停滞问题。目前的工作在数学上模拟了这个交替的过程。采用CEC 2014基准函数对PEPSO的效率进行了评价。实验表明,该算法能够解决早熟收敛问题,在某些测试函数上具有较好的收敛精度。此外,还证实了蜂群被分成不同的群体,每个群体的个体几乎都是一致的。这为PSO与组织行为之间的关系提供了一个新的实验视角。
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