NL-SHADE-LBC algorithm with linear parameter adaptation bias change for CEC 2022 Numerical Optimization
Vladimir V. Stanovov, Shakhnaz Akhmedova, Eugene Stanislavovich Semenkin · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
In this paper the adaptive differential evolution algorithm is presented, which includes a set of concepts, such as linear bias change in parameter adaptation, repetitive generation of points for bound constraint handling, as well as non-linear population size reduction and selective pressure. The proposed algorithm is used to solve the problems of the CEC 2022 Bound Constrained Single Objective Numerical Optimization bench-mark problems. The computational experiments and analysis of the results demonstrate that the NL-SHADE-LBC algorithm presented in this study is able to demonstrate high efficiency in solving complex optimization problems compared to the winners of the previous years' competitions.