Plowing PSO: A Novel Approach to Effectively Initializing Particle Swarm Optimization
Particle swarm optimization (PSO) ii an optimization algorithm that has received much attention in recent yean. PSO is a simple and computationally inexpensive algorithm Inspired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multimodal functions. To improve PSO performance on global optimization problems, this paper proposes a novel approach, called Plowing PSO algorithm, through Introducing a new operator to PSO. The proposed approach combines the exploration ability of random search with the features of PSO. Our approach is validated using ten common complex unimodal/multimodal benchmark functions. The simulation results demonstrate that the proposed approach is superior in avoiding premature convergence to standard PSO, and five variation of it Therefore, the Plowing PSO algorithm is successful in improving standard PSO to solve complex numerical function optimization problems.
Particle swarm optimization Random Search
Mohammad Sadegh Norouzzadeh Mohammad Reza Ahmadzadeh Maziar Palhang
Electrical & Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran
国际会议
成都
英文
705-709
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）