Testing A Multi-Operator based Differential Evolution Algorithm on the 100-Digit Challenge for Single Objective Numerical Optimization

Although over the past one decades, several variants of Differential Evolution (DE) have been introduced for solving the global optimization functions, no single variant of DE shows better performance on a variety of optimization problems. During the last five years, to lighten this deficiency, many variants of DE which employ multiple mutation and crossover strategies in a single structure of algorithm, called as multi-operators variant of DE (MODE), have been proposed. In this work, ESHADE, an enhanced version of a MODE, is introduced including various mutation strategies and an exponential population size reduction (EPSR) technique is utilized to reduce size of the population for the next iteration. Additionally, a version of uni-variate sampling method is employed in later iterations to provide a balance between exploitative and explorative search. To perform the comparative analysis, the proposed algorithm is benchmarked on the problem suite of the 100-digit challenge on single objective numerical optimization at CEC-2019. Comparative analysis reveals that the ESHADE can provide high-quality solutions as compared to state-of-the-art algorithms.