A self-adaptive differential evolutionary algorithm based on population reduction with minimum distance
Ming Yang, Jing Guan, Zhihua Cai, Changhe Li · International Journal of Innovative Computing and Applications · 2014
In differential evolution (DE), many adaptive algorithms have been proposed for parameter adaptation. However, they mainly focus on tuning the mutation factor F and crossover probability CR. The adaptation of population size NP has not been widely studied in the scope of DE. Reducing population size could save computational resources and hence accelerate the convergence speed of algorithms. This is beneficial for algorithms to solve the optimisation problems which need expensive evaluations. However, population reduction may weaken the population diversity, hence, it will result in population premature. In this paper, we propose a novel population reduction method for jDE algorithm, called dynNPMinD-jDE. When the population reduction criterion is satisfied, dynNPMinD-jDE selects the best individual and the pair individuals with minimal-step difference vectors to form a new population. To enhance the population diversity, dynNPMinD-jDE adopts the adaptive mechanisms of F and CR. dynNPMinDjDE is tested on a set of 38 scalable benchmark functions. The results show that dynNPMinD-jDE can get better results on most functions, and the convergence speed becomes faster and faster as each population reduction.