结合莱维飞行和布朗运动的金鹰算法

Golden Eagle Optimizer Algorithm Combining Levy Flight and Brownian Motion

针对金鹰优化算法衰减速度慢和收敛精度低的问题,提出一种结合莱维飞行和布朗运动的金鹰优化算法。对金鹰种群个体引入Fuch混沌映射,对其进行初始化,增加金鹰个体的多样性;在金鹰个体的位置更新公式上引入莱维飞行机制和布朗运动机制,提高搜索精度,帮助金鹰个体跳出局部最优;在金鹰个体的整体位置更新公式上引入衰减因子,提高收敛速度。在14个基准测试函数下分别和9个经典基本算法和5个改进算法进行对比,结果表明:改进的金鹰优化算法拥有更好的性能,在3个工程应用中得到了更好的验证。

Aiming at the slow attenuation and low convergence precision of golden eagle optimization algorithm,a new algorithm combining Levy fight and Brownian motion is proposed.In order to increase the diversity,Fuch chaotic map is introduced to initialize the golden eagle individuals.Levy flight mechanism and Brownian motion mechanism are introduced into the position update formula of golden eagle individual to improve the search accuracy and help to the jump out of local optimum.The reduction factor is introduced into the overall position update formula of the golden eagle individual to improve the convergence speed.Compared with 9 original algorithms and 5 improved algorithms under 14 benchmark test functions,the experimental results show that the improved golden eagle optimization algorithm has better performance,which are verified by three engineering applications.