Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing researc...Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm II(NSGA-II),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method improves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target production optimization problems.展开更多
This study presents a parametric design and optimization approach for bucket drum lunar regolith collector.Using discrete element method(DEM)simulations,the operational performance of the collector was analyzed,focusi...This study presents a parametric design and optimization approach for bucket drum lunar regolith collector.Using discrete element method(DEM)simulations,the operational performance of the collector was analyzed,focusing on filling efficiency,collection rate,and evacuation rate.Three surrogate models—radial basis function(RBF),Gaussian process regression(GPR),and support vector regression(SVR)—were constructed to form a composite surrogate model.The performance of four multi-objective optimization algorithms(MOPSO,NSGA-II,SPEA-II,PESA-II)was compared,with MOPSO demonstrating the best results.An adaptive surrogate model invocation mechanism based on absolute error of leave-one-out cross-validation(AELOOCV)further enhanced optimization accuracy.The entropy weight method and TOPSIS were employed to select the optimal solution from the Pareto set,leading to improvements of 10.353%in filling efficiency,13.275%in collection rate,and 12.070%in evacuation rate.The study highlights the effectiveness of combining surrogate models with advanced optimization algorithms in lunar soil collection design.展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant 52274057,52074340 and 51874335the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSNthe Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002111 Project under Grant B08028.
文摘Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm II(NSGA-II),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method improves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target production optimization problems.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFF0500300,2023YFB3711300)the Strategic Research and Consulting Project of the Chinese Academy of Engineering(Grant Nos.2023-XZ-90,2023-JB-09-10)the Space Application System of China Manned Space Program.
文摘This study presents a parametric design and optimization approach for bucket drum lunar regolith collector.Using discrete element method(DEM)simulations,the operational performance of the collector was analyzed,focusing on filling efficiency,collection rate,and evacuation rate.Three surrogate models—radial basis function(RBF),Gaussian process regression(GPR),and support vector regression(SVR)—were constructed to form a composite surrogate model.The performance of four multi-objective optimization algorithms(MOPSO,NSGA-II,SPEA-II,PESA-II)was compared,with MOPSO demonstrating the best results.An adaptive surrogate model invocation mechanism based on absolute error of leave-one-out cross-validation(AELOOCV)further enhanced optimization accuracy.The entropy weight method and TOPSIS were employed to select the optimal solution from the Pareto set,leading to improvements of 10.353%in filling efficiency,13.275%in collection rate,and 12.070%in evacuation rate.The study highlights the effectiveness of combining surrogate models with advanced optimization algorithms in lunar soil collection design.
