Nonlinear continuous global optimization by modified differential evolution

Abstract

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and population size. It is reported that DE is sensitive to the choice of these parameters. To improve the quality of the solution, in this paper, we propose a modified differential evolution introducing self-adaptive parameters, modified mutation and the inversion operator. We test our method with a set of nonlinear continuous optimization problems with simple bounds.