Affiliations: [a] Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India | [b] Department of Mathematics, AMITY University, Gurgaon, India
Correspondence:
[*]
Corresponding author: Pooja, Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India. Tel.: +91 8859 966903; E-mail:[email protected]
Abstract: Differential Evolution (DE) is considered as a simple yet
influential search engine used for optimization of real-valued, multimodal
and nonlinear functions. Here two new variants of the parent DE are
presented with self-tuned control parameters and a modified mutation
scheme. The First variant, designed by applying self-adaptive control
parameters to the parent DE, is named as CPDE and the second one, the
enhanced version of CPDE in which CPDE is embedded with a modified mutation
scheme, is named as CPMDE. The crucial role of the self-adaptive control
parameters can't be abandoned, which is used to improve the quality of the
solution. The next application of self-adapted control parameters is drawn
with a modified mutation operation in which the whole search space is
divided into three equal parts for the sake of maximum exploration of the
search space. A set of 14 traditional and 12 non-traditional (6 shifted and
6 hybrid) test problems is chosen for validation of the performance of the
proposed algorithms which is then compared with the parent DE and some other
variants of DE in terms of number of function evaluations, CPU time, error
and standard deviation. Numerical and statistical results show that the
proposed algorithm helps in providing a better trade-off between convergence
rate and efficiency.
Keywords: Differential evolution, control parameters, mutation, global optimization
