多策略改进的龙格库塔优化算法  

Multi-strategy improved Runge Kutta optimization algorithm

作　　者：高晗 吴芸[1] 刘祚鑫 江海新[1] GAO Han;WU Yun;LIU Zuoxin;JIANG Haixin(School of Science,Jiujiang University,Jiujiang 332005,China)

机构地区：[1]九江学院理学院,江西九江332005

出　　处：《高师理科学刊》2024年第7期5-14,51,共11页Journal of Science of Teachers＇College and University

基　　金：江西省自然科学基金项目(20224BAB201010);江西省教育厅科学技术研究项目(GJJ211823,GJJ211825,GJJ201814);江西省大学生创新创业训练计划项目(S202111843039);九江学院大学生创新创业训练计划项目(X202311843014)。

摘　　要：针对龙格库塔优化算法存在收敛速度较慢和易陷入局部最优等问题,提出多策略改进的龙格库塔优化算法.引入混合反向学习策略扩大种群的寻优范围,增强算法的搜索能力,借助莱维飞行策略增强算法跳出局部最优的能力,同时引入动态调节因子更有效地平衡算法的开发和探索能力.在15个基准测试函数上展开多维度数值实验并进行Wilcoxon秩和检验,实验结果表明,所提算法相较对比算法而言具有更好的寻优性能.此外,焊接梁设计问题上的测试实验进一步验证了多策略改进的龙格库塔优化算法在工程问题上的可行性与有效性.Aiming at the shortcomings of the Runge Kutta optimization algorithm,such as slow convergence speed and easy to fall into local optimum,a multi-strategy improved Runge Kutta optimization algorithm is proposed.A hybrid opposition-based learning strategy is introduced to expand the optimization range of the population and enhance the search ability of the algorithm,then the Levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimum,and the dynamic adjustment factor is introduced to balance the exploitation and exploration ability of the algorithm more effectively.Finally,multi-dimensional numerical experiments are carried out on 15 benchmark functions and Wilcoxon rank-sum test is performed,and the experimental results show that the proposed algorithm has a better optimisation performance compared with the comparative algorithms.In addition,the test experiments on the welded beam design problem further verify the feasibility and effectiveness of multi-strategy improved Runge Kutta optimization algorithm on engineering problems.

关 键 词：龙格库塔优化算法 混合反向学习 莱维飞行 动态调节因子 焊接梁设计 

分 类 号：O241[理学—计算数学]