Two hybrid differential evolution algorithms for engineering design optimization |
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Authors: | T. Warren Liao |
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Affiliation: | 1. Department of Instrumental and Electrical Engineering, Xiamen University, China;2. State Key Lab. of Electr. Insulation & Power Equip., Xi’an Jiaotong University, Xi’an, China;3. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK;4. Shen Zhen Graduate School, Tsinghua University, Shenzhen, China;1. Faculty of Computers and Information, Minia University, Minia, Egypt;2. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt;3. Department of Electrical Engineering, Université de Sherbrooke, Sherbrooke, Canada |
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Abstract: | This paper presents two hybrid differential evolution algorithms for optimizing engineering design problems. One hybrid algorithm enhances a basic differential evolution algorithm with a local search operator, i.e., random walk with direction exploitation, to strengthen the exploitation ability, while the other adding a second metaheuristic, i.e., harmony search, to cooperate with the differential evolution algorithm so as to produce the desirable synergetic effect. For comparison, the differential evolution algorithm that the two hybrids are based on is also implemented. All algorithms incorporate a generalized method to handle discrete variables and Deb's parameterless penalty method for handling constraints. Fourteen engineering design problems selected from different engineering fields are used for testing. The test results show that: (i) both hybrid algorithms overall outperform the differential evolution algorithms; (ii) among the two hybrid algorithms, the cooperative hybrid overall outperforms the other hybrid with local search; and (iii) the performance of proposed hybrid algorithms can be further improved with some effort of tuning the relevant parameters. |
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