Adaptive multi-group fruit fly optimization algorithm

Abstract

Aiming at the defects that the basic fruit fly optimization algorithm has low control precision and is easy to fall into local optimum, an adaptive multi-group fruit fly optimization algorithm is proposed. Due to the constant step size, the basic fruit fly algorithm has a lack of convergence efficiency and optimization precision. For this problem, the radius adjustment coefficient is introduced in the search process, so that the search radius decreases with the increase of iterations. In order to avoid the premature phenomenon caused by the lack of population diversity in the search process, the degree of utilization of the whole information during the evolution of the population is improved by simultaneously learning the local optimal individual and the global optimal individual of the subpopulation. At the same time, adding individual variation mechanism to further increase the diversity of the population makes the algorithm jump out of the local optimal solution. The simulation results show that the proposed algorithm has better performance in terms of convergence efficiency and optimization accuracy.