期刊
INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING
卷 10, 期 1, 页码 1-28出版社
IGI GLOBAL
DOI: 10.4018/IJAMC.2019010101
关键词
Constrained Optimization; Differential Evolution; Engineering Optimization; Handling Constraints; Novel Mutation
The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC' 2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.
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