Abstract: In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic Fruit fly Optimization Algorithm（FOA）, an improved FOA called self-adapting adjustment of the iteration step value FOA（FOAMR） is proposed. The evolution speed factor and aggregation degree factor of the FOA are introduced in this new algorithm and the iteration step value[R]is formulated as a function of these two factors and adaptive adjustment factor is newly defined. At each iteration process, the iteration step value[R]can be changed dynamically based on evolution speed factor and aggregation degree factor and the searching distance changes with the variation of the adaptive adjustment factor. The experimental results show that the new algorithm has the advantages of better global searching ability, faster convergence and more precise convergence.

Key words: Fruit fly Optimization Algorithm（FOA）, iteration step value, evolution speed factor, aggregation degree factor

摘要： 针对传统果蝇优化算法（FOA）收敛精度不高和易陷入局部最优的缺点，提出了一种迭代步进值自适应调整的果蝇优化算法（FOAMR）。在该算法中，引入了果蝇群体速度进化因子和聚集度因子，并将迭代步进值表示为以上2个参数的函数同时定义自适应调整因子。在每次迭代时，算法根据当前果蝇群体速度进化因子和聚集度因子动态调整步进值的大小并通过自适应调整因子动态调整搜索距离的大小。对典型函数的测试结果表明，FOAMR比FOA具有更好的全局搜索能力，同时收敛速度、收敛精度明显提高。

关键词: 果蝇优化算法, 迭代步进值, 速度进化因子, 聚集度因子