果蝇优化算法(FOA)有易陷入局部最优和应用领域小的不足之处.通过修改味道浓度判定值,并将整个迭代寻优过程分为果蝇移动范围逐步增大和逐步减小两阶段,给出了一种改进的果蝇优化算法(IFOA).相对于FOA,IFOA扩大了应用领域,保证了果蝇群体的多样性,也使果蝇更易跳出局部最优.最后对于6个经典测试函数的仿真试验结果表明,IFOA具有更好的全局搜索能力,在收敛精度、收敛速度和收敛稳定性上均比FOA有较大提高. Drosophila optimization algorithm (FOA) is easy to fall into the local optimum and the application of small deficiencies.By modifying the taste concentration determination value, and the whole iterative optimization process is divided into Drosophila mobile range gradually increase and gradually reduce the two stages , An improved fruit fly optimization algorithm (IFOA) is given. Compared with FOA, IFOA enlarges the application field, assures the diversity of fruit fly population and makes it easier for fruit flies to jump out of the local optimum.Finally, The simulation results of the test function show that IFOA has a better global search ability, and has a greater improvement on convergence accuracy, convergence speed and convergence stability than FOA.