摘要
为提高粒子群算法的优化效率,在分析量子粒子群优化算法的基础上,提出了一种随机粒子群优化算法。该算法只有一个控制参数,搜索步长由一个随机变量的取值动态决定,通过合理设计控制参数的取值,实现对目标位置的跟踪。标准测试函数极值优化和聚类优化的实验结果表明,与量子粒子群和普通粒子群算法相比,该算法在优化能力和优化效率两方面都有改进。
To improve the efficiency of particle swarm optimization, a random particle swarm optimization algorithm is proposed on the basis of analyzing the search process of quantum particle swarm optimization lgorithm. The proposed algorithm has only a parameter, and its search step length is controlled by a random variable value. In this model, the target position can be accurately tracked by the reasonable design of the control parameter. The experimental results of standard test function extreme optimization and clustering optimization show that the proposed algorithm is superior to the quantum particle swarm optimization and the common particle swarm optimization algorithm in optimization ability and optimization efficiency.
出处
《计算机系统应用》
2012年第2期245-248,217,共5页
Computer Systems & Applications
基金
黑龙江省教育厅科学技术研究项目(11551015)
关键词
随机粒子群优化
粒子群优化
群智能优化
仿生智能优化
算法设计
random particle swarm optimization
particle swarm optimization
swarm intelligent optimization
bionicintelligent optimization
algorithm design
