针对传统灰狼优化算法位置更新时勘探与开发失衡,收敛速度慢且陷入局部最优的问题,提出一种改进的灰狼算法(balanced grey wolf algorithm based on fitness back learning,BGWO),引入非线性控制参数,增强算法前期勘探能力,加速收敛;在...针对传统灰狼优化算法位置更新时勘探与开发失衡,收敛速度慢且陷入局部最优的问题,提出一种改进的灰狼算法(balanced grey wolf algorithm based on fitness back learning,BGWO),引入非线性控制参数,增强算法前期勘探能力,加速收敛;在种群迭代阶段采用重心反向学习的最优适应度权重更新策略,平衡算法的勘探与开发。16组基准函数测试结果表明,改进后算法能自适应跳出局部最优,在加快算法收敛速度的同时提高全局收敛能力与精度。将BGWO应用于PV型旋风分离器粒级效率GBDT(gradient boosting decision tree)的建模,提高了GBDT的精度,模型相关系数0.980,均方误差0.00079,BGWO-GBDT与GBDT、PSO-GBDT和GWO-GBDT相对比,建模精度和稳定性明显提高,验证了BGWO的有效性。展开更多
Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a si...Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.展开更多
文摘针对传统灰狼优化算法位置更新时勘探与开发失衡,收敛速度慢且陷入局部最优的问题,提出一种改进的灰狼算法(balanced grey wolf algorithm based on fitness back learning,BGWO),引入非线性控制参数,增强算法前期勘探能力,加速收敛;在种群迭代阶段采用重心反向学习的最优适应度权重更新策略,平衡算法的勘探与开发。16组基准函数测试结果表明,改进后算法能自适应跳出局部最优,在加快算法收敛速度的同时提高全局收敛能力与精度。将BGWO应用于PV型旋风分离器粒级效率GBDT(gradient boosting decision tree)的建模,提高了GBDT的精度,模型相关系数0.980,均方误差0.00079,BGWO-GBDT与GBDT、PSO-GBDT和GWO-GBDT相对比,建模精度和稳定性明显提高,验证了BGWO的有效性。
基金Project(52072412)supported by the National Natural Science Foundation of China。
文摘Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.
