X-MODE: Extended Multi-operator Differential Evolution algorithm

Abstract

During past years, multi-operator optimization techniques gained vast attention due to their flexible structure and self-adaptation. They yielded promising results when compared with the single-operator based methods. However, the existing multi-operator models can be further improved regarding exploration–exploitation equilibrium. Therefore, we propose a novel multi-operator algorithm for solving single-objective optimization problems that categorize differential evolution mutation strategies according to the purpose each of them fulfills. A new mutation scheme is also presented to promote the mutation across search space boundaries. A novel extended crossover technique is proposed in this study to retain the properties of applied mutation strategies, as well as develop the potential of the standard crossover function. The model is afterward evaluated on the CEC2020 benchmark test-suite. The proposed algorithm has overwhelming results while comparing single-operator and multi-operator algorithms for several classes of functions. It has been observed from the study that proposed mutation and crossover techniques help improve the efficiency of MODE variants.