动态调整概率的双重布谷鸟搜索算法

布谷鸟搜索算法是一种新兴的仿生智能算法,存在着求解精度低、易陷入局部最优及收敛速度慢等缺陷,提出了动态调整概率的双重布谷鸟搜索算法(DECS)。首先,在自适应发现概率P中引入了种群分布熵,通过算法的所处迭代阶数和种群分布情况,动态改变发现概率P的大小,有利于平衡布谷鸟算法局部寻优和全局寻优的能力,加快收敛速度;其次,在布谷鸟寻窝的路径位置更新公式中,采用了一种新型步长因子更新寻优方式,形成Levy飞行双重搜索模式,充分搜索空间;最后,在随机偏好游走的更新公式引入非线性对数递减的惯性权重策略,使得算法有效克服易陷入局部最优的缺陷,提高寻优搜索能力。与4种算法相比和19个测试函数的仿真结果表明:改进布谷鸟算法的寻优性能明显提高,收敛速度更快,求解精度更高,具有更强的全局搜索能力和跳出局部最优能力。

Double Cuckoo Search Algorithm with Dynamically Adjusted Probability

Cuckoo search algorithm is an emerging bionic intelligent algorithm,which has the shortages of low search precision,easy to fall into local optimum and slow convergence speed.Double cuckoo search algorithm with dynamically adjusted probability(DECS)is proposed.Firstly,the population distribution entropy is introduced into the adaptive discovery probability P,and the size of the discovery probability P is dynamically changed by the iteration order of the algorithm and the population distribution situation.It is advantageous to balance the ability of cuckoo algorithm local optimization and global optimization and accelerate the convergence speed.Secondly,in the formula for updating the path position of cuckoo's nest search,a new step-size factor update and optimization method is adopted to form a double search mode of Levy flight,which sufficiently searches the solution space.Finally,the nonlinear logarithmic decreasing inertial weight is introduced into the updated formula of stochastic preference walk,so that the algorithm can effectively overcome the shortcoming of being easily trapped into a local optimum,and improve search ability.Compared with four algorithms,simulation results of 19 test functions show that,the optimization performance of the improved cuckoo algorithm is significantly improved,the convergence speed is faster,the solution accuracy is higher,and it has stronger ability of global search and jumping out of local optimum.