计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

求解无约束优化问题的改进布谷鸟搜索算法

苏芙华1a,刘云连1b,2,伍铁斌1b   

  1. (1. 湖南人文科技学院 a. 物理与电子信息系;b. 机电工程系,湖南 娄底 417000; 2. 湖南科技大学信息与电气工程学院,湖南 湘潭 411201)
  • 收稿日期:2013-09-22 出版日期:2014-05-15 发布日期:2014-05-14
  • 作者简介:苏芙华(1976-),女,讲师、硕士,主研方向:智能控制,安全监测;刘云连,助教;伍铁斌(通讯作者),讲师、博士研究生。
  • 基金项目:
    国家自然科学基金资助项目(61174113);湖南省教育厅基金资助一般项目(13C433);湖南省科技厅基金资助项目(2014GK3033, 2013FJ6073)。

Modified Cuckoo Search Algorithm for Solving Unconstrained Optimization Problem

SU Fu-hua  1a, LIU Yun-lian  1b,2, WU Tie-bin  1b   

  1. (1a. Department of Physics and Electronic Information; 1b. Department of Electrical and Mechanical Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China; 2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)
  • Received:2013-09-22 Online:2014-05-15 Published:2014-05-14

摘要: 布谷鸟搜索算法是一种基于种群迭代搜索的全局优化算法。为求解无约束优化问题,提出一种改进的布谷鸟搜索算法。利用混沌序列构造初始种群以增加群体的多样性,引入动态随机局部搜索技术对当前最优解进行局部搜索,以加快算法的收敛速度。对4个标准测试函数进行仿真实验,并与其他6种算法进行比较,结果表明,该算法具有较强的全局搜索能力和较快的收敛速度。

关键词: 布谷鸟搜索算法, 无约束优化问题, 混沌, 动态随机局部搜索, 惯性权重, 多样性

Abstract: Cuckoo Search(CS) algorithm is proposed as a population-based optimization algorithm and it is so far successfully applied in a variety of fields. A modified CS algorithm is proposed for solving unconstrained optimization problems. Chaos sequence and dynamic random local search technique are introduced to enhance the optimization ability and to improve the convergence speed of CS algorithm. Through testing the performance of the proposed algorithm on a set of 4 benchmark functions and comparing with other six algorithms, simulation result shows that the proposed algorithm has great ability of global search and better convergence rate.

Key words: Cuckoo Search(CS) algorithm, unconstrained optimization problem, chaotic, dynamic random local search, inertia weight, diversity

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