《计算机应用研究》|Application Research of Computers

免疫综合学习粒子群优化算法

Immune comprehensive learning particle swarm optimization algorithm

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作者 林国汉,章兢,刘朝华
机构 1.湖南大学 电气与信息工程学院,长沙 410082;2.湖南工程学院 电气信息学院,湖南 湘潭 411101;3.湖南科技大学 信息与电气工程学院,湖南 湘潭 411021
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文章编号 1001-3695(2014)11-3229-05
DOI 10.3969/j.issn.1001-3695.2014.11.007
摘要 针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优,则无法跳出等缺陷,提出免疫综合学习粒子群优化(ICLPSO)算法。ICLPSO算法引入人工免疫系统中的克隆选择机制,利用克隆复制、高频变异、克隆选择等操作,增加种群的多样性,提高算法的收敛速度,利用柯西分布较宽的两翼分布特性进行精英粒子学习以进一步增强粒子逃离局部极值及多峰函数优化问题全局寻优能力。针对标准测试函数的仿真结果表明,与其他改进粒子群算法相比,ICLPSO算法收敛速度快,求解精度更高。
关键词 综合学习粒子群算法(CLPSO);人工免疫系统;精英学习;函数优化
基金项目 国家自然科学基金资助项目(61174140)
国家教育部博士点基金资助项目(20110161110035)
湖南省自然科学基金资助项目(11jj4049)
本文URL http://www.arocmag.com/article/01-2014-11-007.html
英文标题 Immune comprehensive learning particle swarm optimization algorithm
作者英文名 LIN Guo-han, ZHANG Jing, LIU Zhao-hua
机构英文名 1. College of Electrical & Information Engineering, Hunan University, Changsha 410082, China; 2. College of Electrical & Information, Hunan Institute of Engineering, Xiangtan Hunan 411101, China; 3. School of Information & Electrical Engineering, Hunan University of Science & Technology, Xiangtan Hunan 411021, China
英文摘要 Convergence of the comprehensive learning particle swarm optimization(CLPSO) algorithm is relatively slow at the late stage of evolution. Once all particles trapped in local optimum, the algorithm can not jump out of the local optimum. This paper proposed immune comprehensive learning particle swarm optimization(ICLPSO) algorithms. The algorithm introduced clonal selection mechanism in artificial immune system. Using of clonal copy, hypermutation and clonal selection, it increased the diversity of the population, improved the convergence rate and enhanced the ability of escape from the local optimum and multi-mode optimization ability of global optimization. Using the elitist learning strategy, the ability to escape from local optimia is further enhanced. Experiments on several benchmark functions verify the effective of the proposed algorithm.
英文关键词 comprehensive learning particle swarm optimization algorithm; artificial immune system; elitist learning; function optimization
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收稿日期 2013/10/28
修回日期 2013/12/19
页码 3229-3233
中图分类号 TP301.6
文献标志码 A