横波可控震源振动器平板作为页岩气勘探中的关键部件,其疲劳寿命直接影响可控震源的使用寿命和勘探精度。然而,传统的振动器平板疲劳寿命优化方法未考虑平板与平板齿间焊接残余应力的影响,导致平板结构在抗疲劳优化设计方面效果不佳。为...横波可控震源振动器平板作为页岩气勘探中的关键部件,其疲劳寿命直接影响可控震源的使用寿命和勘探精度。然而,传统的振动器平板疲劳寿命优化方法未考虑平板与平板齿间焊接残余应力的影响,导致平板结构在抗疲劳优化设计方面效果不佳。为此,使用局部灵敏度法对平板疲劳寿命进行敏感性分析,确定了焊接残余应力为影响疲劳寿命的关键因素。随后,建立了平板的各向最大焊接残余应力与焊接速度和焊接层间温度之间的数学模型,并以各向最大焊接残余应力为约束,以疲劳寿命为优化目标,建立相应的优化模型。最后,利用NSGA-Ⅱ(nondominated sorting genetic algorithm-Ⅱ,非支配排序遗传算法-Ⅱ)获取Pareto解集,并结合熵权法和TOPSIS(technique for order preference by similarity to ideal solution,逼近理想解排序)法确定最佳优化方案:焊接速度为10.23 mm/s,焊接层间温度为105℃。结果表明,优化后平板的疲劳寿命为10.23年,相比优化前提高了17.72%。研究结果可为横波可控震源振动器平板的疲劳寿命优化提供科学有效的理论方法和工程指导。展开更多
This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platfo...This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.展开更多
调谐质量阻尼器(tuned mass damper,TMD)的减振效率与结构和TMD的固有参数相关,准确从结构-TMD耦合系统响应中识别结构和TMD固有参数是对在役TMD减振性能评价的必要条件。提出了一种基于NSGA-Ⅱ(非支配排序遗传算法,nondominated sortin...调谐质量阻尼器(tuned mass damper,TMD)的减振效率与结构和TMD的固有参数相关,准确从结构-TMD耦合系统响应中识别结构和TMD固有参数是对在役TMD减振性能评价的必要条件。提出了一种基于NSGA-Ⅱ(非支配排序遗传算法,nondominated sorting genetic algorithm)的参数识别方法,从结构-TMD耦合运动响应中识别“裸结构”和“裸TMD”的固有参数,进而实现对在役状态TMD的减振性能评估。该方法构建了结构-TMD耦合运动方程,并将其减缩和转化为结构被控模态和TMD耦合的两自由度系统,借助系统状态空间矩阵搭建两个目标函数,通过遗传算法寻找理论值与试验值的最小误差所对应的最优解,从而识别结构和TMD的固有参数。开展了单自由度结构-TMD耦合系统和多自由度结构-TMD耦合系统参数识别数值仿真分析,结果表明:提出的方法可以从耦合系统动力响应中准确识别结构和TMD的固有参数。展开更多
联邦学习是一种新型的分布式机器学习方法,可以在不共享原始数据的前提下训练模型。当前,联邦学习方法存在针对模型准确率最优化、通信成本最优化、参与者性能分布均衡等多个目标同时优化难的问题,难以做到多目标的同步均衡。针对该问题...联邦学习是一种新型的分布式机器学习方法,可以在不共享原始数据的前提下训练模型。当前,联邦学习方法存在针对模型准确率最优化、通信成本最优化、参与者性能分布均衡等多个目标同时优化难的问题,难以做到多目标的同步均衡。针对该问题,提出联邦学习四目标优化模型及求解算法。将全局模型错误率、模型准确率分布方差、通信成本、数据成本作为优化目标,构建优化模型。同时,针对该模型的求解搜索空间大,传统NSGA-Ⅲ算法难以寻优的问题,提出基于佳点集初始化策略的改进NSGA-Ⅲ联邦学习多目标优化算法GPNSGA-Ⅲ(Good Point Set Initialization NSGA-Ⅲ),以求取Pareto最优解。该算法通过佳点集初始化策略将有限的初始化种群以均匀的方式分布在目标求解空间中,相较于原始算法,使第一代解最大限度地接近最优值,提升寻优能力。实验结果证明,GPNSGA-Ⅲ算法得到的Pareto解的超体积值相较于NSGA-Ⅲ算法平均提升107%;Spacing值相较于NSGA-Ⅲ算法平均下降32.3%;对比其他多目标优化算法,GPNSGA-Ⅲ算法能在保证模型准确率的情况下,更有效地实现模型分布方差、通信成本和数据成本的均衡。展开更多
This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective o...This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.展开更多
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ...The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.展开更多
文摘横波可控震源振动器平板作为页岩气勘探中的关键部件,其疲劳寿命直接影响可控震源的使用寿命和勘探精度。然而,传统的振动器平板疲劳寿命优化方法未考虑平板与平板齿间焊接残余应力的影响,导致平板结构在抗疲劳优化设计方面效果不佳。为此,使用局部灵敏度法对平板疲劳寿命进行敏感性分析,确定了焊接残余应力为影响疲劳寿命的关键因素。随后,建立了平板的各向最大焊接残余应力与焊接速度和焊接层间温度之间的数学模型,并以各向最大焊接残余应力为约束,以疲劳寿命为优化目标,建立相应的优化模型。最后,利用NSGA-Ⅱ(nondominated sorting genetic algorithm-Ⅱ,非支配排序遗传算法-Ⅱ)获取Pareto解集,并结合熵权法和TOPSIS(technique for order preference by similarity to ideal solution,逼近理想解排序)法确定最佳优化方案:焊接速度为10.23 mm/s,焊接层间温度为105℃。结果表明,优化后平板的疲劳寿命为10.23年,相比优化前提高了17.72%。研究结果可为横波可控震源振动器平板的疲劳寿命优化提供科学有效的理论方法和工程指导。
基金financially supported by the National Natural Science Foundation of China(Grant No.52371261)the Science and Technology Projects of Liaoning Province(Grant No.2023011352-JH1/110).
文摘This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.
文摘联邦学习是一种新型的分布式机器学习方法,可以在不共享原始数据的前提下训练模型。当前,联邦学习方法存在针对模型准确率最优化、通信成本最优化、参与者性能分布均衡等多个目标同时优化难的问题,难以做到多目标的同步均衡。针对该问题,提出联邦学习四目标优化模型及求解算法。将全局模型错误率、模型准确率分布方差、通信成本、数据成本作为优化目标,构建优化模型。同时,针对该模型的求解搜索空间大,传统NSGA-Ⅲ算法难以寻优的问题,提出基于佳点集初始化策略的改进NSGA-Ⅲ联邦学习多目标优化算法GPNSGA-Ⅲ(Good Point Set Initialization NSGA-Ⅲ),以求取Pareto最优解。该算法通过佳点集初始化策略将有限的初始化种群以均匀的方式分布在目标求解空间中,相较于原始算法,使第一代解最大限度地接近最优值,提升寻优能力。实验结果证明,GPNSGA-Ⅲ算法得到的Pareto解的超体积值相较于NSGA-Ⅲ算法平均提升107%;Spacing值相较于NSGA-Ⅲ算法平均下降32.3%;对比其他多目标优化算法,GPNSGA-Ⅲ算法能在保证模型准确率的情况下,更有效地实现模型分布方差、通信成本和数据成本的均衡。
文摘This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.
基金in part supported by the Key Research and Development Project of Hubei Province(Nos.2020BAB1141,2023BAB094)the Key Project of Science and Technology Research ProgramofHubei Educational Committee(No.D20211402)+1 种基金the Teaching Research Project of Hubei University of Technology(No.XIAO2018001)the Project of Xiangyang Industrial Research Institute of Hubei University of Technology(No.XYYJ2022C04).
文摘The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.
