关键词: 果蝇优化算法（FOA）, 群智能算法, 动态步长, 收敛精度

Abstract:

Aiming at the shortages of basic Fruit Fly Optimization Algorithm（FOA） with slow convergence speed, easy to fall into local optimum and low search precision, a new Fruit Fly Optimization Algorithm with Dynamic Adjustment of Search Strategy（FOAASS） is proposed. Firstly, chaotic map is used to enhance the uniformity and randomness of the initial distribution of fruit fly population. Secondly, the search strategies of some fruit flies are dynamically adjusted according to the population evolutionary information. Then, the search radius is randomly selected by conversion probability and dynamically adjusted. Finally, when the algorithm falls into premature, the search strategy is changed to jump out of local optimum. The experimental results show that the proposed algorithm is much better than basic FOA and its several improved algorithms in convergence precision and convergence rate.

Key words: Fruit Fly Optimization Algorithm（FOA）, swarm intelligent algorithm, dynamic step size, convergence precision