Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation
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 o...
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| Published in | Journal of bionics engineering Vol. 15; no. 4; pp. 751 - 763 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Singapore
Springer Singapore
01.07.2018
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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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| AbstractList | 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. |
| Author | Xu, Liwu Wang, Chao Beng, Gooi Hoay Liu, Nian Li, Kaicheng Li, Yuanzheng Jiang, Zhiqiang |
| Author_xml | – sequence: 1 givenname: Liwu surname: Xu fullname: Xu, Liwu organization: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical Engineering and Electronics, Huazhong University of Science and Technology – sequence: 2 givenname: Yuanzheng surname: Li fullname: Li, Yuanzheng email: Yuanzheng_Li@hust.edu.cn organization: School of Automation, Ministry of Education Key Laboratory of Image Processing and Intelligence Control, Huazhong University of Science and Technology – sequence: 3 givenname: Kaicheng surname: Li fullname: Li, Kaicheng organization: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical Engineering and Electronics, Huazhong University of Science and Technology – sequence: 4 givenname: Gooi Hoay surname: Beng fullname: Beng, Gooi Hoay organization: Nanyang Technological University – sequence: 5 givenname: Zhiqiang surname: Jiang fullname: Jiang, Zhiqiang organization: School of Hydropower and Information Engineering, Huazhong University of Science and Technology – sequence: 6 givenname: Chao surname: Wang fullname: Wang, Chao organization: China Institute of Water Resources and Hydropower Research – sequence: 7 givenname: Nian surname: Liu fullname: Liu, Nian organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University |
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