Interpolation Based Mutation Variants of Differential Evolution

Abstract

Differential evolution algorithm (DE) is an efficient and versatile population-based search technique for global optimization. In this paper, two novel mutation variants for DE are presented. These mutation variants are based on interpolation rules; first variant is based on Inverse Quadratic Interpolation called IQI-DE and the second variant is based on sequential parabolic interpolation called SPI-DE. Both variants aim at efficiently generating the base vector in the mutation phase of DE. The performance of proposed variants is implemented on 12 benchmark problems and compares with basic DE and five other enhanced versions of DE such as DERL, ODE, jDE, JADE, and LeDE. Experimental results show that the proposed variants are significantly better or at least comparable to other variants in term of convergence speed and solution accuracy.