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

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