关键词: 果蝇优化算法, 修正浓度, 适应步长, 局部最优, 寻优精度

Abstract: The basic fruit fly optimization algorithm can only be positive when solving the optimization problem, and it can not optimize the problem when the concentration is negative. In addition, the step size of the basic fruit fly optimization algorithm is random in iterative optimization, which makes the algorithm precocious and fall into the local optimal solution, and the accuracy of the algorithm is not high. Aiming at these problems of the basic fruit fly algorithm, a modified concentration and adaptive step algorithm for fruit fly is proposed. This algorithm modifies the concentration of fruit fly and makes the taste concentration distribution in the whole range of positive and negative optimization. In iteration, the search distance of fruit fly is changed adaptively by making full use of the global factors that have been carried out in fruit fly population. In order to verify the effectiveness of the algorithm, several commonly used test functions are selected to verify the algorithm. The results show that the algorithm can not only effectively avoid falling into local optimum, but also improve the precision of optimization.

Key words: fruit fly optimization algorithm, modified concentration, adaptive step, local optimum, precision of optimization