Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (7): 43-47.

• 研究、探讨 • Previous Articles     Next Articles

γ-PSO algorithm for solving high-dimensional constrained optimization problems

ZHANG Huibin, WANG Hongbin, DI Dongquan   

  1. Department of Computer Science and Technology, Xinzhou Teachers College, Xinzhou, Shanxi 034000, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-03-01 Published:2012-03-01

一种求解高维约束优化问题的γ-PSO算法

张慧斌,王鸿斌,邸东泉   

  1. 忻州师范学院 计算机科学与技术系,山西 忻州 034000

Abstract: PSO algorithm is one of random searching swarm intelligence algorithm for solving multi-dimensional constrained optimization problem. But when the constraints become more, PSO algorithm is easy to fall into local minimum and slow convergence. In response to these problems, γ-PSO algorithm is proposed, an improved PSO algorithm, which extends random numbers from (0, 1) to (-1, 1). In this way, the PSO algorithm can avoid local minimum by increasing flying speed and diversity of flying direction of particle. Finally, the results of experiments using γ-PSO algorithm for solving high-dimensional constrained optimization problems show that the γ-PSO algorithm can converge to the global optimum, and its convergence is superior to other improved PSO algorithms and other optimization algorithms.

摘要: PSO算法是一种随机搜索的群体智能算法,在求解高维约束优化问题,尤其是在约束条件较多时,PSO算法易陷入局部极值且收敛速度慢。针对上述问题,对PSO算法进行了改进,提出了γ-PSO算法,把PSO算法的随机数由(0,1)扩展到(-1,1),这样加大了粒子飞行速度和飞行方向的多样性,从而使PSO算法具有摆脱局部极值的能力。对γ-PSO算法进行了求解高维约束优化问题的实验,实验结果表明γ-PSO算法能收敛到全局最优值,收敛性能明显优于其他改进的PSO算法和其他优化算法。