基于Levy飞行改进蝴蝶优化算法

Improved Butterfly Optimization Algorithm based on Levy Flight

蝴蝶优化算法是一种模仿蝴蝶觅食行为群智能优化算法,充分利用蝴蝶的嗅觉来确定食物源的位置上,但是该算法与其他智能算法一样,也存在一些缺点和不足如收敛速度和求解精度等方面的问题,蝴蝶优化算法原理主要主要模仿蝴蝶种群寻找食物,每只蝴蝶都散发出一定浓度的香气,每只蝴蝶都会感受到周围其它蝴蝶的味道,并朝着那些散发更多香味的蝴蝶移动而进行搜索.本文提出一种基于Levy飞行改进蝴蝶优化算法,因为levy飞行就是随机游走并且可以在任意维度的空间中,在一个点随机地向任意方向前进任意长度的距离,该算法通过蝴蝶在搜索的过程引进Levy飞行搜索算子来改进原算法局部搜索和全局搜索的能力,最后通过标准测试函数,结果表明收敛速度和求解精度都有所提高.

The butterfly optimization algorithm is an intelligent optimization algorithm that imitates the foraging behavior of butterflies,making full use of the butterfly’s sense of smell to determine the location of the food source.However,this algorithm,like other intelligent algorithms,has some shortcomings and deficiencies such as convergence speed and solution accuracy In other aspects,the principle of butterfly optimization algorithm mainly imitates the butterfly population to find food.Each butterfly emits a certain concentration of aroma,and each butterfly will feel the smell of other butterflies around it and move towards those butterflies that emit more fragrance.Move and search.This paper proposes an improved butterfly optimization algorithm based on Levy flight,because levy flight is a random walk and can move a point in any dimensional space randomly to any direction by an arbitrary length of moment away.The algorithm uses the butterfly search process The Levy flying search operator is introduced to improve the local search and global search capabilities of the original algorithm.Finally,the standard test function is passed.The results show that the convergence speed and solution accuracy have been improved.