山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (5): 74-80.doi: 10.6040/j.issn.1672-3961.0.2023.262
• 机器学习与数据挖掘 • 上一篇
王辰龑1,2,刘轩1,2*,超木日力格1,2
WANG Chenyan1,2, LIU Xuan1,2*, Chaomurilige1,2
摘要： 为提高天牛须搜索算法(beetle antennae search algorithm, BAS)寻优能力,提出一种自适应的并行天牛须优化算法(adaptive and parallel beetle antennae optimization algorithm, APBAO),该算法将BAS中的单只迭代体进化为并行的多只迭代体,尽可能扩大解空间的搜索范围;提出精英天牛的概念实现算法自适应,提高算法精度。为验证算法的性能,采用多个标准测试函数进行测试,将APBAO与BAS、粒子群优化算法(particle swarm optimization, PSO)和蚁群优化算法(ant colony optimization, ACO)的性能进行比较。试验结果表明,与BAS相比,APBAO对目标函数的优化率提高了97.39%,与PSO和ACO相比分别提高了84.46%和86.98%。所提出方法可以有效避免目标函数陷入局部最小值,拥有更好的性能和更强的寻优能力。
中图分类号:
| [1] TILMA S V, JOHANNES E. CON: the hypotension prediction index is not a validated predictor of hypotension[J]. European Journal of Anaesthesiology, 2024, 41(2): 118-121. [2] SAMAD K. Numerical algorithm to Caputo type time–space fractional partial differential equations with variable coefficients[J]. Mathematics and Computers in Simulation, 2021, 182: 66-85. [3] GAO Yuanchen, WANG Bin, CHEN Fei, et al. Multi-step wind speed prediction based on LSSVM combined with ESMD and fractional-order beetle swarm optimization[J]. Energy Reports, 2023, 9: 6114-6134. [4] JIANG Xiangyuan, LI Shuai. BAS: beetle antennae search algorithm for optimization problems[J]. International Journal of Robotics and Control, 2018, 1(1):1-1. [5] SINGH P, KAUR A, BATTH S R, et al. Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system[J]. Neural Computing and Applications, 2021, 33(16): 1-12. [6] SHEN Han, DU Haibo, ZHOU Jun. Beetle swarm optimization algorithm with adaptive mutation[J]. Journal of Computer Applications, 2020, 40:1-7. [7] WANG Li, CHEN Jili, XIE Xiaolan, et al. Neural network model for classification based on chaotic beetle swarm algorithm[J]. Science Technology and Engineering, 2022, 22(12): 4854-4863. [8] ZHOU Tianjiang, QIAN Qian, FU Yunfa. Fusion simulated annealing and adaptive beetle antennae search algorithm[J]. Communications Technology, 2019, 52(7): 1626-1631. [9] ZHEN Ran, WANG Zhenbo, KAN Hailong, et al. A multi-point positioning algorithm based on improved beetle antennae search algorithm[J]. Radio Engineering, 2022, 52(10): 1765-1774. [10] TANG Tianbing, JIANG Qi, YAN Yi. Hybrid Beetle antennae search algorithm for solvingtraveling salesman problem[J]. Popular Science & Technology, 2021, 23(1): 8-10. [11] QIAN Qian, DENG Yi, SUN Hui, et al. Enhanced beetle antennae search algorithm for complex and unbiased optimization.[J]. Soft Computing, 2022, 26(19): 31-39. [12] FAN Qingsong, HUANG Haisong, LI Yiting, et al. Beetle antenna strategy based grey wolf optimization[J]. Expert Systems with Applications, 2021: 165. [13] WANG Qifa, CHENG Guanhua, SHAO Peng. An adaptive beetle swarm optimization algorithm with novel opposition based Learning[J]. Electronics, 2022, 11(23): 3905-3905. [14] LIU Wenfeng, LI Ang. Three-step composite photovoltaic MPPT algorithm based on IP&OIBSO[J]. Thermal Power Generation, 2022, 51(10): 138-144. [15] LIAO Liefa, YANG Hong. Review of beetle antennae search[J]. Computer Engineering and Applications, 2021, 57(12): 54-64. [16] WANG Zihang, LIU Jianhua, XUE Xingsi, et al. Particle swarm optimization with velocity limit combining iteration and problem dimension[J]. Journal of East China Jiaotong University, 2023, 40(4): 112-126. [17] HAO Zhaoming, AN Pingjuan, LI Hongyan, et al. Mobile robot path planning based on enhanced goal heuristic information ant colony algorithm[J].Science Technology and Engineering, 2023, 23(22): 9585-9591. [18] YANG Yijian, LI Ming, XING Kai, et al. Research on improved pheromone heuristic factor ant colony algorithm for TSP problem[J]. Industrial Control Computer, 2023, 36(7): 82-86. [19] ZHANG Yinyan, LI Shuai, XU Bin. Convergence analysis of beetle antennae search algorithm and its applications[J]. Soft Computing, 2021, 25(16): 1-14. [20] LI Jie, YAN Yuepeng, LIANG Xiaoxin, et al. Research on the Novel Ultra-wideband Power Divider Based onBeetle Antennae Search Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(2): 418-424. |
| [1] | 刘子一,崔超然,孟凡安,林培光. 基于批归一化统计量的无源多领域自适应方法[J]. 山东大学学报 (工学版), 2023, 53(2): 102-108. |
| [2] | 刘丁菠,刘学艳,于东然,杨博,李伟. 面向小样本目标检测任务的自适应特征重构算法[J]. 山东大学学报 (工学版), 2022, 52(6): 115-122. |
| [3] | 武新章,梁祥宇,朱虹谕,张冬冬. 基于CEEMDAN-GRA-PCC-ATCN的短期风电功率预测[J]. 山东大学学报 (工学版), 2022, 52(6): 146-156. |
| [4] | 孟祥飞,张强,胡宴才,张燕,杨仁明. 欠驱动船舶自适应神经网络有限时间跟踪控制[J]. 山东大学学报 (工学版), 2022, 52(4): 214-226. |
| [5] | 许传臻,袭肖明,李维翠,孙仪,杨璐. 基于自适应多分辨率特征学习的CNV分型网络[J]. 山东大学学报 (工学版), 2022, 52(4): 69-75. |
| [6] | 程业超,刘惊雷. 自适应图正则的单步子空间聚类[J]. 山东大学学报 (工学版), 2022, 52(2): 57-66. |
| [7] | 闵海根,方煜坤,吴霞,王武祺. 网联交通环境下的车-车通信故障诊断方法[J]. 山东大学学报 (工学版), 2021, 51(6): 84-92. |
| [8] | 梁启星,李彬,李志,张慧,荣学文,范永. 基于模型预测控制的四足机器人斜坡自适应调整算法与实现[J]. 山东大学学报 (工学版), 2021, 51(3): 37-44. |
| [9] | 杨修远,彭韬,杨亮,林鸿飞. 基于知识蒸馏的自适应多领域情感分析[J]. 山东大学学报 (工学版), 2021, 51(3): 15-21. |
| [10] | 周恺卿,李航程,莫礼平. 基于全局最优的自适应和声搜索算法[J]. 山东大学学报 (工学版), 2021, 51(2): 47-56. |
| [11] | 程春蕊,毛北行. 一类非线性混沌系统的自适应滑模同步[J]. 山东大学学报 (工学版), 2020, 50(5): 1-6. |
| [12] | 王春彦,邸金红,毛北行. 基于新型趋近律的参数未知分数阶Rucklidge系统的滑模同步[J]. 山东大学学报 (工学版), 2020, 50(4): 40-45. |
| [13] | 刘保成,朴燕,宋雪梅. 联合检测的自适应融合目标跟踪[J]. 山东大学学报 (工学版), 2020, 50(3): 51-57. |
| [14] | 张胜男,王雷,常春红,郝本利. 基于三维剪切波变换和BM4D的图像去噪方法[J]. 山东大学学报 (工学版), 2020, 50(2): 83-90. |
| [15] | 闫威,张达敏,张绘娟,辛梓芸,陈忠云. 基于混合决策的改进鸟群算法[J]. 山东大学学报 (工学版), 2020, 50(2): 34-43. |
|
||
