Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can ...Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.展开更多
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
In this work,the multi-fidelity(MF)simulation driven Bayesian optimization(BO)and its advanced form are proposed to optimize antennas.Firstly,the multiple objective targets and the constraints are fused into one compr...In this work,the multi-fidelity(MF)simulation driven Bayesian optimization(BO)and its advanced form are proposed to optimize antennas.Firstly,the multiple objective targets and the constraints are fused into one comprehensive objective function,which facilitates an end-to-end way for optimization.Then,to increase the efficiency of surrogate construction,we propose the MF simulation-based BO(MFBO),of which the surrogate model using MF simulation is introduced based on the theory of multi-output Gaussian process.To further use the low-fidelity(LF)simulation data,the modified MFBO(M-MFBO)is subsequently proposed.By picking out the most potential points from the LF simulation data and re-simulating them in a high-fidelity(HF)way,the M-MFBO has a possibility to obtain a better result with negligible overhead compared to the MFBO.Finally,two antennas are used to testify the proposed algorithms.It shows that the HF simulation-based BO(HFBO)outperforms the traditional algorithms,the MFBO performs more effectively than the HFBO,and sometimes a superior optimization result can be achieved by reusing the LF simulation data.展开更多
In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss ...In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss the number of fighters on both sides,and apply cluster analysis to divide our fighter into the same number of groups as the enemy.On this basis,we sort each of our fighters'different advantages to the enemy fighters,and obtain a series of target allocation schemes for enemy attacks by first in first serviced criteria.Finally,the maximum advantage function is used as the target,and the BOA is used to optimize the model.The simulation results show that the established model has certain decision-making ability,and the BOA can converge to the global optimal solution at a faster speed,which can effectively solve the air combat task assignment problem.展开更多
为准确评估监测条件有限的平原河网小流域河水水质演变趋势,预知水质变化情况,利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据,基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)和双向长短期记忆神经网络(bi-direc...为准确评估监测条件有限的平原河网小流域河水水质演变趋势,预知水质变化情况,利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据,基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)和双向长短期记忆神经网络(bi-directional long short-term memory,BiLSTM)建立了地表水水质预测模型。利用箱线图和Spearman秩相关系数挖掘水质的时空分布规律,划定中间河段4个站点为重点研究区域,NH3—N和TP为治理重点。通过BOA和双向信息传递机制优化LSTM超参数和模型结构,结果显示,用BOA-BiLSTM模型预测,未来4 h NH_(3)—N浓度的均方根误差(root mean squared error,RMSE)分别为0.2132,0.3689,0.3327和0.3740;未来4 h TP浓度的RMSE分别为0.0246,0.0321,0.0422和0.0334。二者较基准LSTM模型的预测结果分别提升了15.8%,10.6%,10.6%,17.1%和22.6%,3.6%,14.8%,11.8%。以磨石桥NH_(3)—N浓度为例,对比了时序预测与加入上下游数据后的多变量预测结果,发现时序预测对监测参数较少的平原河网具有更强的适用性和更高的预测精度。同时结合研究区域现场勘查和地块分类情况,指出生活源、污水收集及处理设施不完善、雨污合流应为整治重点。当监测参数有限时,本文模型有助于提升对水质异常的监管水平,为环境执法、水环境治理提供数据支撑。展开更多
The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of c...The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.展开更多
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
基金This project was supported by the Fund of College Doctor Degree (20020699009)
文摘Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
基金supported by the National Key Research and Development Program of China(2019YFB1803205)the Key Research and Development Project of Shaanxi Province(2019GY-007)+1 种基金the National Natural Science Foundation of China(61801369)the Fundamental R esearch Funds for the Central Universities(XZD012021012)。
文摘In this work,the multi-fidelity(MF)simulation driven Bayesian optimization(BO)and its advanced form are proposed to optimize antennas.Firstly,the multiple objective targets and the constraints are fused into one comprehensive objective function,which facilitates an end-to-end way for optimization.Then,to increase the efficiency of surrogate construction,we propose the MF simulation-based BO(MFBO),of which the surrogate model using MF simulation is introduced based on the theory of multi-output Gaussian process.To further use the low-fidelity(LF)simulation data,the modified MFBO(M-MFBO)is subsequently proposed.By picking out the most potential points from the LF simulation data and re-simulating them in a high-fidelity(HF)way,the M-MFBO has a possibility to obtain a better result with negligible overhead compared to the MFBO.Finally,two antennas are used to testify the proposed algorithms.It shows that the HF simulation-based BO(HFBO)outperforms the traditional algorithms,the MFBO performs more effectively than the HFBO,and sometimes a superior optimization result can be achieved by reusing the LF simulation data.
基金the National Natural Science Foundation of China(No.61074090)。
文摘In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss the number of fighters on both sides,and apply cluster analysis to divide our fighter into the same number of groups as the enemy.On this basis,we sort each of our fighters'different advantages to the enemy fighters,and obtain a series of target allocation schemes for enemy attacks by first in first serviced criteria.Finally,the maximum advantage function is used as the target,and the BOA is used to optimize the model.The simulation results show that the established model has certain decision-making ability,and the BOA can converge to the global optimal solution at a faster speed,which can effectively solve the air combat task assignment problem.
文摘为准确评估监测条件有限的平原河网小流域河水水质演变趋势,预知水质变化情况,利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据,基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)和双向长短期记忆神经网络(bi-directional long short-term memory,BiLSTM)建立了地表水水质预测模型。利用箱线图和Spearman秩相关系数挖掘水质的时空分布规律,划定中间河段4个站点为重点研究区域,NH3—N和TP为治理重点。通过BOA和双向信息传递机制优化LSTM超参数和模型结构,结果显示,用BOA-BiLSTM模型预测,未来4 h NH_(3)—N浓度的均方根误差(root mean squared error,RMSE)分别为0.2132,0.3689,0.3327和0.3740;未来4 h TP浓度的RMSE分别为0.0246,0.0321,0.0422和0.0334。二者较基准LSTM模型的预测结果分别提升了15.8%,10.6%,10.6%,17.1%和22.6%,3.6%,14.8%,11.8%。以磨石桥NH_(3)—N浓度为例,对比了时序预测与加入上下游数据后的多变量预测结果,发现时序预测对监测参数较少的平原河网具有更强的适用性和更高的预测精度。同时结合研究区域现场勘查和地块分类情况,指出生活源、污水收集及处理设施不完善、雨污合流应为整治重点。当监测参数有限时,本文模型有助于提升对水质异常的监管水平,为环境执法、水环境治理提供数据支撑。
基金National Natural Science Foundation of China with the Grant Number 61877045。
文摘The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.
