A New Differential Evolution Algorithm with Random Mutation

摘要

This paper suggests a new differential evolution algorithm with a random mutation. In this algorithm, a new dynamically mutation rule is given through a linear descending weighted convex combination of two different mutation strategies of DE/rand/1 and DE/best/1, so as to use dynamically DE/rand/1 and DE/best/1 advantages, and an exponent increased crossover probability and a random mutation is introduced to further improve the global optimal capacity. The standard test functions tests show that the new algorithm has fast convergence and high accuracy, robustness stronger, more suitable for solving complex high-dimensional global optimization problem.