Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 3, Pages 1551-1572Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2014.09.046
Keywords
Evolutionary computation; Differential evolution algorithm; Discrete mutation parameters; Control parameter adaptation; Mutation strategy adaptation
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Funding
- 973 project of China [2013CB733605]
- National Natural Science Foundation of China [21176073]
- Fundamental Research Funds for the Central Universities
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Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors. (C) 2014 Elsevier Ltd. All rights reserved.
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