Differential evolution with auto-enhanced population diversity: The experiments on the CEC'2016 competition
Ming Yang, Jing Guan, Changhe Li · 2016
For the differential evolution (DE) algorithms, there are many parameter adaptation methods, which aim at tuning the mutation factor F and the crossover probability CR. When the population diversity is very small and has been converged in a local optimum, even if the evolution goes on, the population will no longer improve. This is also true for the DE algorithms with adaptive F and CR. The enhancement of population diversity is necessary to DE algorithms. In this paper, we test the JADE algorithm with auto-enhanced population diversity (AEPD) on the newest benchmark functions.