Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

Ali Wagdy Mohamed, Ali Khater Mohamed, Ehab Z. Elfeky, Mohamed Saleh
Copyright: © 2019 |Pages: 28
DOI: 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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2. Cops Formulation And Handling The Constraints

Generally, COP has the following form (Yong, 2009):Min IJAMC.2019010101.m01, IJAMC.2019010101.m02(1) St.:

IJAMC.2019010101.m03
(2)
IJAMC.2019010101.m04
(3) “Where IJAMC.2019010101.m05, IJAMC.2019010101.m06 is the feasible region, and IJAMC.2019010101.m07 is an IJAMC.2019010101.m08-dimensional rectangular space in IJAMC.2019010101.m09 defined by the parametric constraints IJAMC.2019010101.m10 where IJAMC.2019010101.m11 and IJAMC.2019010101.m12 are lower and upper bounds for a decision variable IJAMC.2019010101.m13, respectively. Most constraint-handling techniques that’s is used in EAs are dealing with inequality constraints only. Therefore, inequality constraints of the form IJAMC.2019010101.m14 are obtained from equality constraints, where IJAMC.2019010101.m15is the tolerance level.” (Mohamed A. W., 2017)

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