[1]刘倩,冯艳红,陈嶷瑛.基于混沌初始化和高斯变异的飞蛾火焰优化算法[J].郑州大学学报(工学版),2021,42(03):53.[doi:10.13705/j.issn.1671-6833.2021.03.009]
　Liu Qian,Feng Yanhong,Chen Yingying,et al.Moth-flame Optimization Algorithm Based on Chaotic Initialization and Gaussian Mutation[J].Journal of Zhengzhou University (Engineering Science),2021,42(03):53.[doi:10.13705/j.issn.1671-6833.2021.03.009]

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基于混沌初始化和高斯变异的飞蛾火焰优化算法(/HTML)

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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

42卷

期数:

2021年03期

页码:

53

栏目:

出版日期:

2021-05-10

文章信息/Info

Title:

Moth-flame Optimization Algorithm Based on Chaotic Initialization and Gaussian Mutation

作者:

刘倩; 冯艳红; 陈嶷瑛

河北地质大学信息工程学院;河北地质大学河北省智能传感物联网技术工程研究中心;

Author(s):

Liu Qian; Feng Yanhong; Chen Yingying;

School of Information Engineering, Hebei University of Geosciences; Hebei University of Geosciences Hebei Provincial Intelligent Sensor IoT Technology Engineering Research Center;

关键词:

混沌初始化; 高斯变异; 阿基米德曲线; 飞蛾火焰优化算法; 群体智能

Keywords:

chaotic initialization;  Gaussian mutation;  Archimedes curve;  moth-flame optimization algorithm;  swarm intelligence

DOI:

10.13705/j.issn.1671-6833.2021.03.009

文献标志码:

A

摘要:

针对飞蛾火焰优化算法(moth-flame optimization algorithm,MF0）在求解最优化问题时存在寻优精度低、易陷入局部最优等问题,提出一种基于混沌初始化和高斯变异的改进飞蛾火焰优化算法。首先,采用立方混沌映射对飞蛾种群进行初始化操作,使飞蛾更均匀地分布于搜索空间:其次,应用高斯变异对种群中少数较差个体进行扰动以增强算法跳出局部最优的能力:最后,通过阿基米德曲线扩大搜索范围,提高算法对未知领域的探索能力。在CEC14测试函数及21个可扩展Benchmark函数上进行了一系列实验,与标准飞蛾火焰优化算法、遗传算法、人工蜂群算法、粒子群算法、差分进化算法、花授粉算法和蝴蝶优化算法进行比较,结果表明,该算法能明显提高解的精度和算法的收敛速度。

Abstract:

Moth-flame optimization algorithm (MFO) has some drawbacks in solving optimization problems, such as low precision and high possibility of being trapped in local optimum. A modified MFO algorithm based on chaotic initialization and Gaussian mutation is proposed. Firstly, the cube chaotic map is used to initialize the moth population, which makes the moth more evenly distributed in the search space. Then, Gaussian mutation is adopted to disturb a few poor individuals to enhance the ability of escaping the local optimum. Finally, Archimedes curve is introduced to expand the search scope and strength the exploration ability in the unknown field. A series of experiments are carried out on CEC14 test function set and 21 extensible Benchmark functions. Compared with standard moth-flame optimization algorithm, genetic algorithm, artificial bee colony algorithm, particle swarm algorithm, differential evolution algorithm, flower pollination algorithm, and butterfly optimization algorithm, the results demonstrate that the proposed algorithm is strengthened in obtaining solutions with better quality and convergence.

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