Parent-centric differential evolution algorithm for global optimization problems

TLDR: Empirical analysis of numerical results show that the performance of proposed versions of (DE) is either at par or better in comparison to TDE and basic DE in terms of convergence rate and quality of fitness function value.

Abstract: Differential evolution (DE) is a population based evolutionary search algorithm widely used for solving optimization problems. In the present article we investigate the application of parent-centric approach on the performance of classical DE, without tampering with the basic structure of DE. The parent-centric approach is embedded in the mutation phase of DE. We propose two versions of (DE) called differential evolution with parent-centric crossover (DEPCX) and differential evolution with probabilistic parent-centric crossover (ProDEPCX) in order to improve the performance of classical DE. The proposed algorithms are validated on a test bed of ten benchmark functions and the numerical results are compared with basic DE and a modified version called trigonometric differential evolution (TDE). Empirical analysis of numerical results on the benchmark problems show that the performance of proposed versions is either at par or better in comparison to TDE and basic DE in terms of convergence rate and quality of fitness function value.