Improved particle swarm optimization based on particles＇ explorative capability enhancement

• Published in: Journal of systems engineering and electronics 2016-08, Vol.27 (4), p.900-911
• Main Authors: Yongjian, Yang, Xiaoguang, Fan, Zhenfu, Zhuo, Shengda, Wang, Jianguo, Nan, Wenkui, Chu
• Format: Article
• Language: English
• Subjects: Benchmarking; Convergence; Electronics; Mathematical models; Optimization; Production methods; PSO算法; State of the art; Swarm intelligence; 优化模型; 局部最优解; 探索能力; 收敛速度; 粒子群优化算法; 粒子群算法; 随机方向
• ISSN: 1004-4132
• DOI: 10.21629/JSEE.2016.04.19

Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.