Differential Evolution with Laplace Mutation Operator

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. Mutation operation plays the most significant role in the performance of a DE algorithm. This paper proposes a simple modified version of classical DE called MDE. MDE makes use of a new mutant vector in which the scaling factor F is a random variable following Laplace distribution. The proposed algorithm is examined on a set of ten standard, nonlinear, benchmark, global optimization problems having different dimensions, taken from literature. The preliminary numerical results show that the incorporation of the proposed mutant vector helps in improving the performance of DE in terms of final convergence rate without compromising with the fitness function value.