Efficiency Improvement of Differential Evolution Algorithm Using a Novel Mutation Method

Abstract

The differential evolution algorithm is one of the fast, efficient, and strong population-based algorithms, which has extended applications in solving various problems. Although the velocity, power, and efficiency of this algorithm have been demonstrated in solving many optimization problems, this algorithm, like other metaheuristic algorithms, is not guaranteed to achieve the global optimal points of the optimization problems and may be ceased at optimal local points. One of the reasons for stopping the algorithm at the local optimum points is the imbalance between the exploration and exploitation abilities of the algorithm. One of the operators of the differential evolution algorithm, which plays an essential role in establishing the proper balance between the exploitation and exploitation of the algorithm, is the mutation operator. In this paper, a new mutation method is proposed to improve the efficiency of the differential evolution algorithm to make an appropriate balance between the exploitation and exploitation abilities of the algorithm. Comparing the results of the proposed mutation method with other mutation methods indicates that the proposed method has better speed and accuracy convergence rather than other methods, and it can be employed to solve large-scale optimization problems.