A New Differential Evolution Algorithm and Its Application to Real Life Problems

Most of the real life problems occurring in various disciplines of science and engineering can be modeled as optimization problems. Also, most of these problems are nonlinear in nature which requires a suitable and efficient optimization algorithm to reach to an optimum value. In the past few years various algorithms has been proposed to deal with nonlinear optimization 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 a modified DE algorithm called DEPCX which uses parent centric approach to manipulate the solution vectors. The performance of DEPCX is validated on a test bed of five benchmark functions and five real life engineering design problems. Numerical results are compared with original differential evolution (DE) and with TDE, another recently modified version of DE. Empirical analysis of the results clearly indicates the competence and efficiency of the proposed DEPCX algorithm for solving benchmark as well as real life problems with a good convergence rate.