Two modified differential evolution algorithms and their applications to engineering design problems

Differential Evolution (DE) is a stochastic, population based search technique, which can be classified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. In order to improve the performance of DE, We propose two versions of (DE) called Differential Evolution with Parent Centric Crossover (DEPCX) and Differential Evolution with probabilistic Parent Centric Crossover (Pro. DEPCX). The proposed algorithms are validated on a test bed of seven real life, nonlinear engineering design problems and numerical results are compared with original differential evolution (DE). Empirical analysis of the results indicates that the proposed schemes enhance the performance of basic DE in terms of convergence rate without compromising with the quality of solution.