Abstract: In order to solve the problems of low convergence precision and convergence rate of traditional fruit fly algorithm, a new improved FOA is proposed. This algorithm, through the iteration procedures, uses the optimal value of the rate as a reference of the fruit fly swarm, affecting the next flight distance. The changing flight distance of the fruit swarm dynamically can balance the global search ability and local search ability of the algorithm effectively. This algorithm is compared with the original FOA and other two improved FOA algorithms in the function optimization simulation process. Experimental results show that the new algorithm has obvious advantages in terms of convergence precision, convergence speed and stability.

Key words: fruit algorithm, optimization, optimal value, changing rate, convergence precision

摘要： 针对传统果蝇优化算法在进行优化时所存在的寻优精度偏低和收敛速度较慢的问题，提出了一种新的改进果蝇优化算法。该算法在迭代过程中将每次迭代所得最优值的变化率作为下一次果蝇种群飞行距离变化的参考依据。动态改变果蝇种群每次飞行的距离，能够有效地权衡算法的全局搜索能力和局部搜索能力。将该改进算法在函数优化中与原果蝇算法和另外两种果蝇改进算法进行仿真对比，结果表明，所提出的改进算法在收敛精度、收敛速度以及稳定性方面具有明显优势。

关键词: 果蝇算法, 优化, 最优值, 变化率, 收敛精度