摘要
针对基本正弦余弦算法在求解复杂优化问题时求解精度偏低,收敛速度慢及不能跳出局部最优等问题,提出了一种双种群纵横交叉正弦余弦算法。在初始化种群阶段引入Logistic混沌映射,使初始种群均匀分布;非线性调整转换参数并改进正弦余弦位置更新公式,以平衡算法全局搜索和局部开发的能力,加快算法的求解速度;采用双种群和择优选择策略,实现正弦余弦种群和纵横交叉种群优势互补、协同进化,提高算法跳出局部最优解的能力和算法收敛速度。采用23个基准测试函数对改进算法进行仿真实验,并与其它智能优化算法进行比较分析,结果表明改进算法有更好的优化性能。
Aiming at the problems of the basic sine cosine algorithm in solving complex optimization problems,such as low so-lution accuracy,slow convergence speed and inability to jump out of local optimality,a bi-group crisscross sine cosine algorithm is proposed.This paper introduces logistic chaostic mapping in the initialization population phase to make the initial population distri-bution more uniform.Non-linear adjustment of the transformation parameters and improvement of the sine cosine position update for-mula to balance the ability of the algorithm to search globally and develop locally to speed up the solution of the algorithm.The bi-group and merit selection strategies are used to realize the complementary advantages and cooperative coevolution of the sine co-sine population and the crisscross population,and to improve the ability of the algorithm to jump out of the local optimal solution and the convergence speed of the algorithm.The improved algorithm is simulated using 23 benchmark test functions and compared with other intelligent optimization algorithms for analysis,and the results show that the improved algorithm has better optimization performance.
作者
杨闯
王联国
YANG Chuang;WANG Lianguo(School of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070)
出处
《计算机与数字工程》
2024年第6期1622-1629,1720,共9页
Computer & Digital Engineering
基金
甘肃省重点研发计划(编号:21YF5GA088)
甘肃省教育信息化建设专项任务项目(编号:2011-02)资助。
关键词
正弦余弦算法
混沌映射
纵横交叉算法
双种群
协同进化
sine cosine algorithm
chaostic map
crisscross optimization algorithm
bi-group
cooperative coevolution
