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.Article Preview
TopGenerally, COP has the following form (Yong, 2009):Min 
, 
(1) St.:
(2) (3) “Where
,
is the feasible region, and
is an
-dimensional rectangular space in
defined by the parametric constraints
where
and
are lower and upper bounds for a decision variable
, respectively. Most constraint-handling techniques that’s is used in EAs are dealing with inequality constraints only. Therefore, inequality constraints of the form
are obtained from equality constraints, where
is the tolerance level.” (Mohamed A. W., 2017)