基于布谷鸟迭代更新策略的多重优化算法  

Multiple Optimization Algorithm Based on Cuckoo Iterative Updating Strategy

作　　者：丁希成 王红军 DING Xicheng;WANG Hongjun(College of Electronic and Engineering,National University of Defense Technology,Hefei 230000,China)

机构地区：[1]国防科技大学电子对抗学院,合肥230000

出　　处：《计算机工程与应用》2022年第9期67-73,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(61971473);湖南省科技创新项目。

摘　　要：针对布谷鸟算法在寻优过程中存在的收敛速度慢和局部探索能力弱的缺点,基于布谷鸟迭代更新策略提出了一种新型的多重优化算法。通过引入Skew tent映射初始化种群,增强种群的多样性。受粒子群算法启发,引入社会学习和自我学习来提高种群的信息交流能力,并采用对数自适应参数平衡来提高全局搜索能力和局部开发能力。借鉴灰狼算法的狩猎机制,引导种群中其他个体进行变异,以进一步提高算法收敛速度。采用8个标准测试函数对算法性能进行了仿真实验,实验结果表明,改进算法的总体性能明显优异于对比算法。Aiming at the shortcomings of slow convergence speed and weak local exploration ability in the optimization process of cuckoo search algorithm,this paper proposes a new multiple optimization algorithm based on cuckoo iterative updating strategy.The improved algorithm introduces Skew tent mapping to initialize the population and enhance the diversity of the population.Inspired by the particle swarm optimization algorithm,the social learning and self-learning are introduced to improve the information communication ability of the population,and the logarithmic adaptive parameter balance is introduced to improve the global search ability and the local development ability.Learning from the hunting mechanism of the gray wolf optimization algorithm,other individuals in the population are guided to mutate to further improve the convergence speed of algorithm.This paper uses 8 standard test functions to simulate the performance of the algorithm.The experiment shows that the overall performance of the improved algorithm is significantly better than the comparison algorithm.

关 键 词：布谷鸟算法 灰狼算法 TENT映射 对数自适应 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]