一种基于lévy飞行轨迹的果蝇优化算法

摘要

针对果蝇优化算法是模仿果蝇寻找食物行为而进行全局搜索最优解的新算法,该算法存在容易陷入局部最优解和收敛速度慢的缺点,提出一种基于lévy飞行轨迹的改进果蝇优化算法. 引入lévy飞行轨迹随机性,将它应用在果蝇算法中的个体嗅觉寻找食物的随机方向上增加搜索的多样性和搜索的范围.最后通过数值仿真实验对8个标准测试函数来进行作对比检验,结果表明该算法在求解高维函数优化问题更好.%Based on the shortcomings of fruit fly optimization Algorithm, which imitates the behavior of flies looking for food, such as low precision, slow convergence rate, and easily falling into local optimal solution, an improvement about the fruit fly optimization algorithm based on lévy flight path is put forward.This method applies the randomness of lévy flight path into the random directions of the individual sense of smell while looking for food, thus increasing the search diversity as well as the search range.At last, a contrast test is conducted to compare the eight standard test functions, the results show that this algorithm is much better in solving the high-dimensional function optimization problems.