摘要： 在果蝇算法的优化过程中，收敛精度会因为初值选取适当与否呈现不稳定状态。针对该问题，提出一种新的混合果蝇算法，该算法融入Logistic映射进行全局搜索得到最优参数值，再以该值为中心在其周围产生微小波动以获取初值进行二次寻优，改进果蝇算法中的初值选取方法。将该混合果蝇算法在函数优化中与原果蝇算法、粒子群算法等进行仿真对比，结果表明其在收敛精度方面具有明显优势。

关键词: 果蝇优化算法, Logistic映射, 最优参数值, 函数优化, 收敛精度

Abstract: In the optimization process of the fruit fly algorithm, the convergence accuracy is unstable because of the initial value choosing suitable or not. In order to solve this problem, this paper proposes a new mixed fruit fly algorithm. The Logistic mapping is integrated into the fruit fly algorithm to do global search for the optimal parameter. It uses the value as the center to do tiny fluctuations to obtain initial value of quadratic optimization, and improve the initial value selection method of fruit fly algorithm. In the function optimization simulation process, compared with the original fruit fly algorithm and Particle Swarm Optimization(PSO) algorithm etc., the convergence accuracy of this mixed fruit fly algorithm has obvious advantages.

Key words: Fruit Fly Optimization Algorithm(FOA), Logistic mapping, best parameter, function optimization, convergence precision

中图分类号: 

• TP301.6