An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

• Published in: Journal of Central South University of Technology. Science & technology of mining and metallurgy Vol. 15; no. 1; pp. 141 - 146
• Main Authors: Li, Xing-mei, Zhang, Li-hui, Qi, Jian-xun, Zhang, Su-fang
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.02.2008
• Subjects: Engineering; Metallic Materials; resource leveling; particle swarm; extended particle swarm optimization algorithm
• ISSN: 1005-9784; 1993-0666
• DOI: 10.1007/s11771-008-0028-5

In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.