A New Differential Evolution Algorithm for Solving Global Optimization Problems

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 new mutant vector based on the concept quadratic interpolation. The proposed algorithm is examined for a set of eleven benchmark, global optimization problems having different dimensions. The numerical results show that the incorporation of the proposed quadratic mutant vector helps in improving the performance of DE in terms of final objective function value and convergence rate.