自适应变异的果蝇优化算法

Fruit fly optimization algorithm with adaptive mutation

针对基本果蝇优化算法(FOA)寻优精度不高和易陷入局部最优的缺点,提出自适应变异的果蝇优化算法(FOAAM)。该算法在运行过程中根据群体适应度方差和当前最优解的大小判断算法陷入局部最优时,首先将最优果蝇个体复制M个;然后对复制的最优果蝇个体进行扰动,按一定的概率P执行高斯变异操作;最后对变异后的最优果蝇个体进行二次寻优,从而跳出局部极值而继续优化。对几种经典测试函数的仿真结果表明,FOAAM算法具有更好的全局搜索能力,在收敛速度、收敛可靠性及收敛精度上均比基本FOA算法有较大的提高。

In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic fruit fly optimization algorithm （ FOA ）, this paper presented an adaptive mutation fruit fly optimization algorithm （FOAAM）. During the evolution, in the condition of basic FOA＇ s trapping in local extremum judging from the population＇ s fitness vari- ance and the current optimal, first, it generated M current optimal replicates. Then, it disturbed replicates by a certain probabili- ty P Gauss mutation operator. Finally, it optimized mutated replicates again to jump out of local extremum and continue to opti- mize. Experimental results show that the new algorithm has the advantages of better global searching ability, speeder conver- gence and more precise convergence.