自适应果蝇优化算法

Self-Adaptive Fruit Fly Optimization Algorithm

摘要:

为了进一步提高果蝇优化算法(FOA)的性能，提出了一种自适应果蝇优化算法(SAFOA)，设计了果蝇搜索群体模型，给出了一种自适应搜索步长搜索算法。仿真结果表明，相比FOA算法和递减步长果蝇优化算法(DS-FOA)，SAFOA收敛速度较快，全局搜索与局部寻优能力强，并能到达高的收敛精确度。

Abstract:

A Self-Adaptive Fruit Fly Optimization Algorithm(SAFOA) is proposed in order to further improve the performance of Fruit Fly Optimization Algorithm(FOA). A fruit fly search group pattern is designed, and then a self-adaptive variable-step search algorithm is put forward. Simulation results indicate that SAFOA features fast rate of convergence, strong global search and local optimization performance, and high convergence precision in comparison with FOA and Diminishing Step Fruit Fly Optimization Algorithm(DS-FOA).