[2018-Vol.15-Issue 4]Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation
Time: 2018-09-03 16:16  Click:151
Journal of Bionic Engineering
Volume 15, Issue 3, May 2018, Pages 751-763.
 
Liwu Xu1, Yuanzheng Li2*, Kaicheng Li1, Gooi Hoay Beng3, Zhiqiang Jiang4, Chao Wang5, Nian Liu6
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
2. School of Automation, Ministry of Education Key Laboratory of Image Processing and Intelligence Control, 
Huazhong University of Science and Technology, Wuhan 430074, China
3. Nanyang Technological University, Singapore 639798, Singapore
4. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
5. China Institute of Water Resources and Hydropower Research, Beijing 100038, China
6. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,
North China Electric Power University, Beijing 102206, China
 
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.

Key words: bioinspired computing      moth-flame optimization      cultural learning      Gaussian mutation      benchmark functions    

Full text is available at :https://link.springer.com/article/10.1007/s42235-018-0063-3

 

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