融合禁忌搜索的混合果蝇优化算法

Fruit fly optimization algorithm based on Tabu search

基本果蝇优化算法(FOA)种群初始位置分布不均匀,搜索后期常跳入局部最优,导致寻优速度慢、寻优精度低,为此融合禁忌搜索的"禁忌"与"特赦"思想进行搜索更新,提出融合禁忌搜索算法(TS)的果蝇优化算法(TSFOA)。将Kent混沌映射的序列作为果蝇种群初始位置,保证果蝇群体在搜索空间中的均匀性、多样性;利用果蝇优化算法进行前期寻优,定义群体适应度方差判断其局部收敛状态;达到局部收敛状态时,引入禁忌搜索,继续深度寻优,提高寻优精度和寻优速度。设计仿真实验测试5个经典标准函数的寻优性能,实验结果表明,TSFOA在寻优精度、寻优速度上均优于基本FOA算法。

Aiming at the problem of uneven distribution of the initial position and easily relapsing into local extremum of basic fruit fly optimization algorithm（FOA）,by introducing the tabo and amnesty of the Tabu search into the evolutionary process of basic FOA,an improved FOA called fruit fly optimization algorithm based on Tabu search（TSFOA）was proposed.Kent sequence of chaotic mapping was used as fruit flies initial population to guarantee uniformity and diversity in the search space of fruit fly species.Fruit flies optimization algorithm was used for the early stage of optimization and group fitness variance was defined to judge the status of local convergence.If the state of local convergence was reached,a Tabu search was introduced to continue depth optimization and improve the optimization precision and the speed of optimization.Experimental results were conducted on five kinds of classical test functions for operation simulation.Results show that the proposed TSFOA is superior to the basic FOA on optimization precision and optimization speed.