NL-SHADE-RSP Algorithm with Adaptive Archive and Selective Pressure for CEC 2021 Numerical Optimization
Vladimir Stanovov, Shakhnaz Akhmedova, Eugene Semenkin
Abstract
This paper proposes a variant of the adaptive differential evolution algorithm, which combines several important concepts, including non-linear population size reduction, rank-based selective pressure in the mutation strategy, adaptive archive set usage, as well as a set of rules to control crossover rate. The developed approach is applied to solve the CEC 2021 Bound Constrained Single Objective Optimization Parametrized Benchmark problems. The performed computational experiments and their statistical analysis show that the proposed NL-SHADE-RSP algorithm is capable of demonstrating high efficiency of fining solutions to biased, shifted and rotated functions compared to other state-of-the-art algorithms, including the winners of the previous competitions.