MDE: Differential evolution with merit-based mutation strategy

Abstract

Currently Differential Evolution (DE) is arguably the most powerful and widely used stochastic population-based real-parameter optimization algorithm. There have been variant DE-based algorithms in the literature since its introduction in 1995. This paper proposes a novel merit-based mutation strategy for DE (MDE); it is based on the performance of each individual in the past and current generations to improve the solution accuracy. MDE is compared with three commonly used mutation strategies on 28 standard numerical benchmark functions introduced in the IEEE Congress on Evolutionary Computation (CEC-2013) special session on real parameter optimization. Experimental results confirm that MDE outperforms the classical DE mutation strategies for most of the test problems in terms of convergence speed and solution accuracy.