基于模拟退火机制的果蝇优化算法

Fruit Fly Optimization Algorithm Based on Simulated Annealing Mechanism

针对基本果蝇优化算法(FOA)易陷入局部最优、寻优精度不高的缺点,提出了基于模拟退火机制的果蝇优化算法(SAFOA)。算法在分析FOA优化机理和局限性的基础上,采用模拟退火机制对果蝇进行扰动,根据随机概率对局部极小点进行取舍和突跳,从而避免果蝇被局部极小值吸引。通过12个标准测试函数的仿真结果表明,相比基本FOA、其它改进的FOA和PSO等算法,SAFOA算法有效地避免了基本FOA算法易陷入局部最优等缺点,具有更强的全局搜索能力和稳定性,其收敛精度、收敛速度均得到显著的提高。

In order to overcome the problems of low convergence precision and easily falling into local optimum in the basic fruit fly optimization algorithm, an improved FOA with the characteristics of simulated annealing mechanism （SAFOA） is proposed in the paper. This paper first analyzes the optimization mechanism and deficiency of the FOA, and then disturbances the fruit flies by using simulated annealing according to the random probability to select and kick the minimum point to avoid being the local optimal solution. The simulation results of 12 standard benchmark functions show that the improved algorithm can avoid being trapped in the local optimum, and has great advantages of the convergence precision, convergence speed and robustness than FOA and some other modified algorithms.