An improved adaptive differential evolution algorithm with population adaptation

In differential evolution (DE), there are many adaptive algorithms proposed for parameters adaptation. However, they mainly aim at tuning the amplification factor F and crossover probability CR. When the population diversity is at a low level or the population becomes stagnant, the population is not able to improve any more. To enhance the performance of DE algorithms, in this paper, we propose a method of population adaptation. The proposed method can identify the moment when the population diversity is poor or the population stagnates by measuring the Euclidean distances between individuals of a population. When the moment is identified, the population will be regenerated to increase diversity or to eliminate the stagnation issue. The population adaptation is incorporated into the jDE algorithm and is tested on a set of 25 scalable CEC05 benchmark functions. The results show that the population adaptation can significantly improve the performance of the jDE algorithm. Even if the population size of jDE is small, the jDE algorithm with population adaptation also has a superior performance in comparisons with several other peer algorithms for high-dimension function optimization.