关键词: 果蝇优化算法, 变步长, 变异, 收敛精度

Abstract: For the demerits of Fruit Fly Optimization Algorithm（FOA）, such as easily relapsing into local optimum, slow convergence rate and low convergence precision, an improved Fruit Fly Optimization Algorithm with Changing Step and Strategy（CSSFOA） is presented. Selecting the historical optimal value randomly as a basis for step changes in a certain range, the changing flight distance of the fruit fly population dynamically can effectively balance the global and local search ability of the algorithm. To avoid falling into local optimum, a certain number of fruit flies are selected to perform variation operation when its population tends to be stable. The experimental results show that the proposed algorithm is much better than basic FOA and its several improved algorithms in convergence rate and convergence precision.

Key words: Fruit Fly Optimization Algorithm（FOA）, changing step, variation, convergence precision