基于黄金正弦与重启机制的二进制樽海鞘算法  

Biometrics Bulletin,1945,1(6):80-83.Binary salp swarm algorithm based on golden sine algorithm and restart mechanism

作　　者：孙中皋[1] 韩雨晴 邹存博 陈然 SUN Zhonggao;HAN Yuqing;ZOU Cunbo;CHEN Ran(School of Physics and Electronic Technology,Liaoning Normal University,Dalian 116029,China)

机构地区：[1]辽宁师范大学物理与电子技术学院,辽宁大连116029

出　　处：《辽宁师范大学学报（自然科学版）》2023年第2期177-185,共9页Journal of Liaoning Normal University:Natural Science Edition

基　　金：辽宁省教育厅科学研究服务地方项目(LF2020003)。

摘　　要：针对樽海鞘算法在解决优化问题时存在收敛速度慢,易陷入局部最优解等问题,提出一种基于黄金正弦与重启机制的二进制樽海鞘改进算法.利用佳点集策略进行种群初始化,提高种群多样性;使用黄金正弦算法对樽海鞘领导者位置进行二次更新,提升算法的收敛速度和精度;在追随者位置更新过程中引入重启机制,防止算法陷入局部最优.为验证改进算法的性能,选取8个经典基准函数进行仿真实验,通过标准差评估、Wilcoxon检验及收敛速度对比等方法进行对比分析,结果表明,与现有5种二进制群优化算法相比,改进算法的收敛速度和寻优精度均有明显提升.The convergence speed of the binary salp swarm algorithm(BSSA)is slow when solving the optimization problem,and it is easy to fall into local optimum.For this,an improved algorithm,called golden sine and restart mechanism binary salp swarm algorithm(GRBSSA)is proposed.The good point set strategy is used to initialize the population,which can increase the diversity of the initial population.The golden sine algorithm is used to update the leaders’position second time to improve the convergence speed and accuracy of the algorithm and the restart mechanism is introduced during the follower position update process to prevent the algorithm from falling into a local optimum.In order to verify the performance of GRBSSA,eight classical benchmark functions are selected for simulation experiments.Through standard deviations,Wilcoxon’s test,convergence speed comparison and other analysis methods,the results show that the convergence speed and optimization accuracy of the proposed algorithm are significantly improved compared with the five existing binary group optimization algorithms.

关 键 词：群优化算法 樽海鞘算法 黄金正弦 重启机制 佳点集 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]