Hsieh Sheng-Ta, Sun Tsung-Ying, Liu Chan-Cheng, Tsai Shang-Jeng
Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan.
IEEE Trans Syst Man Cybern B Cybern. 2009 Apr;39(2):444-56. doi: 10.1109/TSMCB.2008.2006628. Epub 2008 Dec 16.
The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as Quadric, Griewanks, Rastrigin, Ackley, and Weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO.
粒子群优化算法（PSO）是一种基于群体的优化技术，可应用于广泛的问题。本文提出了一种传统PSO算法的变体，称为粒子群优化的高效群体利用策略（EPUS - PSO），采用群体管理器来显著提高PSO的效率。这是通过在群体中使用可变粒子来增强搜索能力并更有效地驱动粒子来实现的。此外，构建了共享原则以防止粒子陷入局部最小值，并使粒子更容易找到全局最优解。针对单峰和多峰测试函数进行了实验，如二次函数、格里沃克斯函数、拉斯特林函数、阿克利函数和魏尔斯特拉斯函数，有无坐标旋转的情况均有涉及。结果表明，与PSO的其他最新变体相比，EPUS - PSO在解决大多数基准问题时表现良好。
