Adaptive Differential Evolution with variable population size for solving high-dimensional problems

In this paper, we present a novel Differential Evolution (DE) algorithm to solve high-dimensional global optimization problems effectively. The proposed approach, called DEVP, employs a variable population size mechanism, which adjusts population size adaptively. Experiments are conducted to verify the performance of DEVP on 19 high-dimensional global optimization problems with dimensions 50, 100, 200, 500 and 1000. The simulation results show that DEVP out performs classical DE, CHC (Crossgenerational elitist selection, Heterogeneous recombination, and Cataclysmic mutation), G CMA-ES (Restart Covariant Matrix Evolutionary Strategy) and GODE (Generalized Opposition-Based DE) on the majority of test problems.