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Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation

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Abstract

This paper presents an Enhanced Moth-Flame Optimization (EMFO) technique based on Cultural Learning (CL) and Gaussian Mutation (GM). The mechanism of CL and the operator of GM are incorporated to the original algorithm of Moth-Flame Optimization (MFO). CL plays an important role in the inheritance of historical experiences and stimulates moths to obtain information from flames more effectively, which helps MFO enhance its searching ability. Furthermore, in order to overcome the disadvantage of trapping into local optima, the operator of GM is introduced to MFO. This operator acts on the best flame in order to generate several variant ones, which can increase the diversity. The proposed algorithm of EMFO has been comprehensively evaluated on 13 benchmark functions, in comparison with MFO. Simulation results verify that EMFO shows a significant improvement on MFO, in terms of solution quality and algorithmic reliability.

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Acknowledgment

The work is supported by National Natural Science Foundation of China (Grant No. 51707069), the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (Grant No. LAPS18001), National Natural Science Foundation of China (Grant No. 51277080), MOE Key Laboratory of Image Processing and Intelligence Control, Wuhan, China (Grant No. IPIC2015-01), and State Key Program of National Natural Science Foundation of China (Grant No.51537003).

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Authors and Affiliations

  1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical Engineering and Electronics, Huazhong University of Science and Technology, Wuhan, 430074, China

    Liwu Xu & Kaicheng Li

  2. School of Automation, Ministry of Education Key Laboratory of Image Processing and Intelligence Control, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yuanzheng Li

  3. Nanyang Technological University, Singapore, 639798, Singapore

    Gooi Hoay Beng

  4. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Zhiqiang Jiang

  5. China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

    Chao Wang

  6. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China

    Nian Liu

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  1. Liwu Xu
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  2. Yuanzheng Li
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  3. Kaicheng Li
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  4. Gooi Hoay Beng
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  7. Nian Liu
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Correspondence to Yuanzheng Li.

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Xu, L., Li, Y., Li, K. et al. Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation. J Bionic Eng 15, 751–763 (2018). https://doi.org/10.1007/s42235-018-0063-3

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  • DOI: https://doi.org/10.1007/s42235-018-0063-3

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