摘要
为了进一步改善算法搜索过程中存在的求解精度偏低、收敛速度缓慢等现象,提出具有动态步长和发现概率的布谷鸟搜索算法。该算法通过引入步长调整因子动态约束每一代种群的莱维移动步长,使算法的莱维飞行机制具有自适应性。在发现概率上,使用具有均匀分布和F分布特性的随机惯性权重,改变发现概率的固定取值,加强种群的多样性,保持算法全局搜索、局部探索之间的平衡状态。通过实验证明,所提算法具有良好的可行性,其寻优结果、收敛速度均有提高。
In order to further improve the low accuracy and slow convergence speed of algorithm search, a cuckoo search algorithm with dynamic step size and probability of discovery is proposed. The algorithm dynamically constrains the Levy’s moving step of each generation by introducing the step adjustment factor, which makes the Levy’s flight mechanism adaptive. In the probability of finding, the random inertia weight with uniform distribution and F distribution is used to change the fixed value of the probability of discovery, to strengthen the diversity of the population and to keep the balance between global search and local exploration. The experiment result proves that the proposed algorithm has a good feasibility, and the optimization results and the convergence speed of the algorithm increase.
作者
刘景森
刘晓珍
李煜
Liu Jingsen;Liu Xiaozhen;Li Yu(Institute of Intelligent Network System,Henan University,Kaifeng 475004,China;College of Software,Henan University,Kaifeng 475004,China;Institute of Management Science and Engineering,Henan University,Kaifeng 475004,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2020年第2期289-298,共10页
Journal of System Simulation
基金
河南省科技攻关重点项目(162102110109)
