一种改进的爬行动物搜索算法  

An Improved Reptile Search Algorithm

作　　者：杜兴丽 刘玲 袁平[1] DU Xingli;LIU Ling;YUAN Ping(School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang 621010,Sichuan,China;Educational Informationization Office,Southwest University of Science and Technology,Mianyang 621010,Sichuan,China)

机构地区：[1]西南科技大学计算机科学与技术学院,四川绵阳621010 [2]西南科技大学教育信息化推进办公室,四川绵阳621010

出　　处：《西南科技大学学报》2023年第3期82-88,共7页Journal of Southwest University of Science and Technology

摘　　要：针对爬行动物搜索算法存在早熟收敛、易陷入局部最优等问题,提出一种改进的爬行动物搜索算法(LERSA)。通过精英反向学习策略提高初始种群的质量,在种群位置更新求解适应度值的过程中加入Levy飞行策略对种群中个体位置进行更新,结合非线性加权策略改良控制参数平衡RSA算法的全局搜索与局部搜索能力。使用公开的性能验证函数、秩和检验及三杆桁架问题进行算法性能测试,结果表明改进后的算法具有良好的寻优性能,能有效解决工程优化问题。An improved reptile search algorithm(LERSA)was proposed to address the problems of premature convergence and easy falling into local optimum in reptile search algorithm.The elite opposition-based learning strategy was used to improve the quality of the initial population,the Levy flight strategy was added to update the individual positions in the population during the process of fitness value solution for population position update,and the nonlinear weighting strategy was combined to improve the control parameters to balance the global search and local search ability of RSA algorithm.The algorithm performance test was carried out by using the public performance verification function,rank sum test,and three-bar truss problem.The results show that the improved algorithm has good optimization performance and can effectively solve engineering optimization problems.

关 键 词：爬行动物搜索算法 精英反向学习 Levy飞行 非线性加权策略 

分 类 号：T391[一般工业技术]