Adaptive component crossover for differential evolution in solving single-objective optimization problems

¹ Reshetnev Siberian State University of Science and Technology, Institute of Informatics and Telecommunications, Krasnoyarsk, Russia
² Siberian Federal University, Institute of Space and Information Technology, Krasnoyarsk, Russia

^* Corresponding author: antonsopov2004@gmail.com

Abstract

With the increase of the complexity of engineering problems, evolutionary algorithms became an effective approach to black-box optimization problems. One of the most popular and promising evolutionary methods is the Differential Evolution algorithms. This method involves several evolutionary operators, including crossover, which is used to form offspring based on mutant and parent vectors, and is important in forming new generations of solutions. However, the classic differential evolution and its numerous modifications usually tends to use the single crossover mechanism to each of the variables of the system, therefore the properties and role of the subcomponents are not considered. That may lead to a slower convergence and increasing demands on computing resources. In this study we have proposed a novel Adaptive Component Crossover strategy for differential evolution, in which the crossover rate parameter is represented by a vector and its values are based on the behavior of the objective function on separate components. The experimental results on a set of benchmark problems have shown that the proposed scheme can improve the performance of the algorithm and, in particular, increase the convergence speed and crossover success rate.