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多策略改进的龙格库塔优化算法

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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.
Multi-strategy improved Runge Kutta optimization algorithm
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.

Runge Kutta optimization algorithmhybrid opposition-based learningLevy flighdynamic adjustment factorwelded beam design

高晗、吴芸、刘祚鑫、江海新

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九江学院 理学院,江西 九江 332005

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

江西省自然科学基金项目江西省教育厅科学技术研究项目江西省教育厅科学技术研究项目江西省教育厅科学技术研究项目江西省大学生创新创业训练计划项目九江学院大学生创新创业训练计划项目

20224BAB201010GJJ211823GJJ211825GJJ201814S202111843039X202311843014

2024

高师理科学刊
齐齐哈尔大学

高师理科学刊

影响因子:0.351
ISSN:1007-9831
年,卷(期):2024.44(7)