改进步长与策略的果蝇优化算法

Improved fruit fly optimization algorithm with changing step and strategy

针对基本果蝇优化算法(FOA)容易陷入局部最优、收敛速度慢和寻优精度不高的缺点,提出了改进步长与策略的果蝇优化算法(CSSFOA)。在一定范围内随机选取历史最优值作为步长变化依据,动态改变果蝇群体的搜寻半径,有效权衡了算法的全局与局部搜索能力;为了避免陷入局部最优,在果蝇群体趋于稳定时选取一定数量的果蝇个体执行变异操作。仿真实验结果表明,提出的改进算法在收敛速度和寻优精度上较基本FOA及其几种改进算法有更好的寻优性能。

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.