一种利用子猫群交互的改进猫群优化算法  

Cat swarm optimization algorithm based on information interaction of subgroup

作　　者：李松阳 于海鹏 王淼 LI Songyang;YU Haipeng;WANG Miao(College of Software,Henan University of Engineering,Zhengzhou 451191,China)

机构地区：[1]河南工程学院软件学院,河南郑州451191

出　　处：《河南工程学院学报（自然科学版）》2022年第2期71-77,80,共8页Journal of Henan University of Engineering：Natural Science Edition

基　　金：国家自然科学基金项目(61501174,U1704124);河南省高等学校重点科研项目(21A520005,20A520010);河南工程学院科研培育基金项目(PYXM202020)。

摘　　要：由于猫群优化算法中搜寻子猫群和跟踪子猫群缺乏交互,影响其收敛速度和收敛精度,故设计一种搜寻子猫群和跟踪子猫群信息交互策略,充分利用已搜索空间,实现普通猫群向优秀猫群学习,从而提高算法的全局搜索能力和收敛速度。在10个标准测试函数上,对比分析了该算法与原始猫群优化(CSO)算法、基于动态搜索的自适应猫群(ADSCSO)算法的平均值、标准差和最优解。实验结果表明,改进后的算法在绝大多数测试函数上的全局搜索能力和收敛速度有显著提升。Because of the lack of interaction between seeking mode and tracking mode cats in cat swarm optimization,its convergence speed and accuracy are affected.An information interaction strategy is designed between seeking mode and tracking mode cats.The strategy makes full use of the search space of seeking mode and tracking mode cats,realizes the ordinary cat group to learn from the excellent cat group to improve the global search ability and convergence speed of the algorithm.On 10 standard test functions,the average value,standard deviation and optimal value of the algorithm are compared with the original cat swarm optimization(CSO)algorithm and the adaptive cat swarm algorithm based on dynamic search(ADSCSO).Experimental results show that the global search ability and convergence speed of the proposed algorithm are significantly improved on most test functions.

关 键 词：猫群优化算法 信息交互 动态分组 教与学优化算法 学习者策略 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]