Differential evolution with improved individual-based parameter setting and selection strategy |
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| Affiliation: | 1. School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, Shaanxi 710119, China;2. School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi 710119, China;1. Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada;2. Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada;1. Indian Statistical Institute, 203 B. T. Road, Kolkata,700 108, India;2. College of IT Engineering, Kyungpook National University, Daegu,702 701, Republic of Korea;3. Dept. of Computer Science & Technology, IIEST, Shibpur, Howrah,711 103, India;1. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), Jyväskylä 40014, Finland;2. Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, England, United Kingdom |
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| Abstract: | In this paper, a novel differential evolution (DE) algorithm is proposed to improve the search efficiency of DE by employing the information of individuals to adaptively set the parameters of DE and update population. Firstly, a combined mutation strategy is developed by using two mixed mutation strategies with a prescribed probability. Secondly, the fitness values of original and guiding individuals are used to guide the parameter setting. Finally, a diversity-based selection strategy is designed by assembling greedy selection strategy and defining a new weighted fitness value based on the fitness values and positions of target and trial individuals. The proposed algorithm compares with eight existing algorithms on CEC 2005 and 2014 contest test instances, and is applied to solve the Spread Spectrum Radar Polly Code Design. Experimental results show that the proposed algorithm is very competitive. |
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| Keywords: | Differential evolution Global optimization Combined mutation strategy Parameters setting Selection strategy |
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