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Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

Ali Wagdy Mohamed, Ali Khater Mohamed, Ehab Z. Elfeky, Mohamed Saleh

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)10(1)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566069|DOI: 10.4018/IJAMC.2019010101

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MLA

Mohamed, Ali Wagdy, et al. "Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems." IJAMC vol.10, no.1 2019: pp.1-28. http://doi.org/10.4018/IJAMC.2019010101

APA

Mohamed, A. W., Mohamed, A. K., Elfeky, E. Z., & Saleh, M. (2019). Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems. International Journal of Applied Metaheuristic Computing (IJAMC), 10(1), 1-28. http://doi.org/10.4018/IJAMC.2019010101

Chicago

Mohamed, Ali Wagdy, et al. "Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.1: 1-28. http://doi.org/10.4018/IJAMC.2019010101

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Abstract

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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Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

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