Differential Evolution Mutations: Taxonomy, Comparison and Convergence Analysis

Abstract

During last two decades, Differential Evolution (DE) proved to be one of the most popular and successful evolutionary algorithms for solving global optimization problems over continuous space. Proposing new mutation strategies to improve the optimization performance of (DE) is considered a significant research study. In DE, mutation operation plays a vital role in the performance of the algorithm. Therefore, in this paper, comprehensive analysis of the contributions on basic and novel mutation strategies that were proposed between 1995 and 2020 is presented. A new taxonomy based on the structure of the novel mutations is proposed. Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with classical DE schemes and recent mutations schemes are executed. Furthermore, theoretical, and empirical convergence behavior analysis of all mutations is discussed. The paper also presents many recommendations, guidelines, insights, and suggestions for experienced practitioners and interested researchers in designing and developing effective and efficient DE algorithms to address various optimization problems in different fields.