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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。
In order to solve the problem that fruit fly optimization algorithm is a new algorithm to simulate fruit flies looking for food behavior to search global optimal solution, this algorithm has the shortcoming of easy to fall into local optimal solution and slow convergence speed, and proposes an improved fruit fly based on lévy flight trajectory optimization. The introduction of lévy flight trajectory randomness, it will be applied in Drosophila algorithm individual smell to find food random orientation to increase search diversity and search range. Finally, eight standard test functions are compared by numerical simulation. The results show that the algorithm is better in solving the problem of high-dimensional function optimization.