摘要： 在传统果蝇优化算法中,果蝇的新位置常被限定在特定区域内,因此,寻优结果对搜索半径依赖性强,导致算法极易陷入局部最优。为此,提出一种改进的果蝇优化算法。将果蝇在每个维度上的搜索范围分为2个部分,给出近郊区和远郊区的概念,引入局部最优导向因子,通过动态调整该因子协调果蝇在不同区域的搜索强度,通过随机选择果蝇位置向量中特定维度实现果蝇位置更新。仿真实验结果表明,与传统自适应混沌果蝇优化算法相比,该算法能有效避免搜寻半径的影响,且在收敛精度、收敛速度等方面具有明显优势。

关键词: 果蝇优化算法, 局部最优, 搜寻半径, 收敛精度, 收敛速度

Abstract: In the traditional Fruit Fly Optimization Algorithm(FOA),the new positions of fruit flies are often limited to particular regions,thus the optimization results are highly dependent on the searching radiuses,and are easy to fall into local optimum.On this basis,an improved FOA algorithm is proposed.The search scope of the fruit fly in each dimension is divided into two parts,and the conceptions of near suburb and far suburb are introduced.A local optimum oriented factor is introduced,and the searching intensities of different scopes are coordinated by adjusting this factor.The fruit fly position is updated by randomly selecting a specific dimension of it.Simulation results show that,when compared with traditional adaptive chaos FOA methods,the proposed method can avoid the effect of searching radius effectively,and has evident advantages on convergence accuracy and convergence speed.

Key words: Fruit Fly Optimization Algorithm(FOA), local optimum, searching radius, convergence accuracy, convergence rate

中图分类号: 

• TP391