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

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

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 03 Page number: 53- Column: Public date: 2021-05-10

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

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;

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

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.03.009

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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Last Update: 2021-06-24