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Bayesian-based ant colony optimization algorithm for edge detection 认领 引用
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作者 YU Yongbin ZHONG Yuanjingyang +6 位作者 FENG Xiao WANG Xiangxiang FAVOUR Ekong ZHOU Chen CHENG Man WANG Hao WANG Jingya 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期892-902,共11页
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
关键词 ant colony optimization(ACO) Bayesian algorithm edge detection transfer function.
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Machine learning for soil parameter inversion enhanced with Bayesian optimization 认领 引用 被引量：1
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作者 Anfeng HU Chi WANG +3 位作者 Senlin XIE Zhirong XIAO Tang LI Ang XU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第11期1034-1051,共18页
Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal ... Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process. 展开更多
关键词 ABAQUS software Bayesian optimization Machine learning(ML)algorithms Parameter inversion Settlement prediction
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Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm 认领 引用 被引量：10
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作者 Qi-Hong Feng Shan-Shan Li +2 位作者 Xian-Min Zhang Xiao-Fei Gao Ji-Hui Ni 《Petroleum Science》 SCIE CAS CSCD 2022年第6期2879-2894,共16页
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
关键词 Well production Optimization efficiency Streamline simulation Streamline feature Objective function Bayesian adaptive direct search algorithm
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Prediction of Asphalt Pavement Rutting Depth Based on Multi-Model Fusion of Stacking Algorithm 认领 引用 被引量：1
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作者 Chenhui Peng Jinbiao Tang Derun Zhang 《Structural Durability & Health Monitoring》 EI 2026年第3期156-171,共16页
Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support ... Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support for asphalt pavement design and maintenance.However,accurate prediction of pavement rutting still remains a significant challenge for pavement engineers.This research first selects the loading number,temperature,dynamic modulus,asphalt layer thickness,and base layer type and thickness as candidate features.Data preprocessing,including outlier handling and feature selection,is then performed.Finally,based on the stacking algorithm,a multi-model fusion approach for predicting rutting depth in asphalt pavements is proposed,using ridge regression(RidgeR),K-nearest neighbor(KNN),multilayer perceptron(MLP),and random forest(RF)models as base models,and support vector machine(SVM)as a meta-model.The model is optimized using a Bayesian optimization model.Results demonstrate the feasibility of using correlation analysis for feature selection.Seven features,including axle weight,upper layer temperature,and middle layer modulus,were selected as predictive features.While all the basic models achieved good prediction accuracy,the stacking ensemble model exhibited lower variance and bias,demonstrating superior generalization capability.The asphalt pavement rutting depth prediction method based on the stacking algorithm multi-model fusion proposed in this research can accurately predict the rutting depth. 展开更多
关键词 Rutting prediction asphalt pavement stacking algorithm Bayesian optimization model
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Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion 认领 引用 被引量：1
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作者 Dan Zhang Yu Zhang Yiwen Liang 《Computers, Materials & Continua》 SCIE EI 2023年第8期2317-2336,共20页
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
关键词 Dendritic cell algorithm signal fusion parameter optimization bayesian optimization hyperband
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Air Combat Assignment Problem Based on Bayesian Optimization Algorithm 认领 引用 被引量：3
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作者 FU LI LONG XI HE WENBIN 《Journal of Shanghai Jiaotong university(Science)》 EI 2022年第6期799-805,共7页
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
关键词 air combat task assignment first in first serviced criteria Bayesian optimization algorithm(BOA)
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基于GWO融合BOA算法的无线传感器节能优化研究 认领 引用
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作者 孔祥恒 顾佳晨 +1 位作者 葛伟 曹逸荣 《自动化与仪器仪表》 2026年第2期184-187,192,共4页
节能设计通过降低传感器节点的能耗延长整个网络系统的生存周期,这对于需要长时间稳定运行的无线传感器网络来说至关重要。为实现上述目的,针对基于GWO融合BOA算法的无线传感器节能优化方法展开研究。结合GWO算法与BOA算法模型定义融合... 节能设计通过降低传感器节点的能耗延长整个网络系统的生存周期,这对于需要长时间稳定运行的无线传感器网络来说至关重要。为实现上述目的,针对基于GWO融合BOA算法的无线传感器节能优化方法展开研究。结合GWO算法与BOA算法模型定义融合方案,在此基础上,规划无线传感器网络的布局形式,实现融合GWO与BOA算法的无线传感器网络优化布置。确定传感器节点分簇模式,并根据节点轮询工作机制,计算循环参数,完成无线传感器节能方法的设计。实验结果表明,应用上述算法布置无线传感器网络,传感节点的存活时间明显延长,转发数据时所需占据的网络带宽也明显下降,符合网络节能的设计初衷。 展开更多
关键词 GWO算法 BOA算法 无线传感器 节能优化 节点分簇 轮询工作机制
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Target distribution in cooperative combat based on Bayesian optimization algorithm 认领 引用 被引量：6
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作者 Shi Zhi fu Zhang An Wang Anli 《Journal of Systems Engineering and Electronics》 2006年第2期339-342,共4页
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
关键词 target distribution Bayesian network Bayesian optimization algorithm cooperative air combat.
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基于BOA-SAM-FTA的超期服役压力管道风险评估 认领 引用
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作者 唐华彬 许磊 +1 位作者 陈思宇 马松华 《中国安全科学学报》 EI CAS CSCD 北大核心 2026年第4期57-64,共8页
为预防超期服役压力管道失效,在故障树分析法(FTA)基础上,提出基于蝴蝶优化算法(BOA)与相似聚合法(SAM)相结合的方法,分析超期服役压力管道失效概率。首先,分析超期压力管道失效原因,建立失效故障树;然后,通过邀请专家评估故障树底事件... 为预防超期服役压力管道失效,在故障树分析法(FTA)基础上,提出基于蝴蝶优化算法(BOA)与相似聚合法(SAM)相结合的方法,分析超期服役压力管道失效概率。首先,分析超期压力管道失效原因,建立失效故障树;然后,通过邀请专家评估故障树底事件,并通过SAM方法和改进BOA-SAM聚合专家意见,计算对比2种方法的不确定性值来评估聚合的准确性(不确定性值越小,聚合结果越精确);最后,计算顶事件失效概率并进行重要度分析。结果表明:改进后的BOA-SAM方法所得聚合结果的不确定性值,均显著低于传统SAM方法,其中最明显的为底事件X6,其不确定性值降低7.6%,顶事件的失效概率为0.117641;应力腐蚀开裂、第三方破坏、焊缝开裂和介质中H2S含量偏高等为事故发生的关键因素。 展开更多
关键词 蝴蝶优化算法(BOA) 相似聚合法(SAM) 故障树分析法(FTA) 超期服役压力管道 风险评估 失效概率
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Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model 认领 引用 被引量：5
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作者 Shu-Yi Du Xiang-Guo Zhao +4 位作者 Chi-Yu Xie Jing-Wei Zhu Jiu-Long Wang Jiao-Sheng Yang Hong-Qing Song 《Petroleum Science》 SCIE EI CAS CSCD 2023年第5期2951-2966,共16页
Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insuffic... Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints. 展开更多
关键词 Production optimization Random forest The Bayesian algorithm Ensemble learning Particle swarm optimization
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Learning Bayesian network structure with immune algorithm 认领 引用 被引量：6
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作者 Zhiqiang Cai Shubin Si +1 位作者 Shudong Sun Hongyan Dui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2015年第2期282-291,共10页
Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorith... Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently. 展开更多
关键词 structure learning Bayesian network immune algorithm local optimal structure vaccination
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基于BOA-SVM的冷源系统温度传感器偏差故障检测 认领 引用 被引量：3
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作者 周璇 闫学成 +1 位作者 闫军威 梁列全 《控制理论与应用》 EI CAS CSCD 北大核心 2025年第5期921-930,共10页
针对当前因温度传感器偏差故障识别率低,严重影响冷源系统节能可靠运行的问题,提出一种基于贝叶斯优化支持向量机BOA-SVM组合优化算法的偏差故障检测方法.该方法融合了贝叶斯优化算法(BOA)和支持向量机(SVM)技术,适用于小样本、非线性... 针对当前因温度传感器偏差故障识别率低,严重影响冷源系统节能可靠运行的问题,提出一种基于贝叶斯优化支持向量机BOA-SVM组合优化算法的偏差故障检测方法.该方法融合了贝叶斯优化算法(BOA)和支持向量机(SVM)技术,适用于小样本、非线性故障数据,同时克服了SVM算法对核函数参数与惩罚因子强敏感性的问题.论文建立了广州市某办公建筑冷源系统Trnsys仿真模型,对室外干球、冷冻供水与冷却进水3种温度传感器不同程度的偏差故障进行模拟.仿真结果表明,与本文提出的其他方法相比,该方法准确率高,泛化能力及鲁棒性强,能够满足冷源系统温度传感器偏差故障的检测需求,保障空调系统的安全、高效与稳定运行. 展开更多
关键词 冷源系统 温度传感器 贝叶斯优化 支持向量机 故障检测 Trnsys
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基于BOA-XGBoost的沥青路面抗滑性能预测方法 认领 引用 被引量：6
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作者 许新权 户媛姣 +1 位作者 翁宇涵 何伟杰 《重庆交通大学学报（自然科学版）》 CAS CSCD 北大核心 2025年第6期35-44,共10页
道路表面纹理是影响抗滑性能的关键因素。为深入研究其影响机理,解决多特征数据条件下传统预测方法精度受限的问题,提出了一种基于贝叶斯优化(BOA)和极端梯度提升(XGBoost)融合的路面抗滑性能评估模型。制备了不同级配类型的沥青混合料... 道路表面纹理是影响抗滑性能的关键因素。为深入研究其影响机理,解决多特征数据条件下传统预测方法精度受限的问题,提出了一种基于贝叶斯优化(BOA)和极端梯度提升(XGBoost)融合的路面抗滑性能评估模型。制备了不同级配类型的沥青混合料试件,基于摆式摩擦仪和三维激光扫描设备分别获取试件表面的摩擦数据和三维纹理数据;提取高度、波长、形状参数用以描述纹理结构,并进行纹理特征重要性分析,明确显著影响抗滑性能因子;引入贝叶斯优化算法的搜索极端梯度来提升模型的最优关键参数,并构建了抗滑性能预估模型。研究结果表明:所提出的模型与对比模型相比,其精度更高,相关系数R2=0.8906,分别比对比模型提升了25.2%、13.0%、15.1%,能有效地关联纹理特征与路面抗滑性能。 展开更多
关键词 道路工程 路面抗滑性能 三维纹理 特征重要性分析 贝叶斯优化算法 极端梯度提升
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基于BOA-BP神经网络的四旋翼飞行器路径优化 认领 引用 被引量：1
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作者 王舒玮 李嘉 +1 位作者 冯健 岳彩宾 《现代防御技术》 北大核心 2025年第3期74-81,共8页
针对四旋翼飞行器在多障碍物环境中飞行时容易出现路径规划不准确的问题,提出了基于蝴蝶算法(BOA)的BP神经网络优化方法。将四旋翼飞行器在设定路径中的所有途经点作为神经网络的训练样本,通过BOA-BP算法对神经网络进行训练,从而确定了... 针对四旋翼飞行器在多障碍物环境中飞行时容易出现路径规划不准确的问题,提出了基于蝴蝶算法(BOA)的BP神经网络优化方法。将四旋翼飞行器在设定路径中的所有途经点作为神经网络的训练样本,通过BOA-BP算法对神经网络进行训练,从而确定了最佳飞行路径。仿真结果表明,与传统的BOA算法相比,所提出的BOA-BP算法模型可以有效减小四旋翼飞行器路径的误差,均方根误差可从1.60%降低到0.003%。 展开更多
关键词 四旋翼 飞行器 蝴蝶优化算法 BP神经网络 路径优化 训练样本 误差处理
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结合不均衡样本生成及BOA-DRSN的扬声器异常声分类 认领 引用 被引量：1
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作者 周静雷 李振业 +1 位作者 路昌 李丽敏 《西安工程大学学报》 CAS 2025年第4期37-45,共9页
扬声器生产过程中,其正常数据与故障数据比例可能会严重失调,从而导致样本分布不均匀,进而影响故障诊断模型的准确率及可靠性。因此,文中根据样本生成扩增和优化深度学习网络的理念提出了一种新的扬声器异常声分类方法。首先,考虑到原... 扬声器生产过程中,其正常数据与故障数据比例可能会严重失调,从而导致样本分布不均匀,进而影响故障诊断模型的准确率及可靠性。因此,文中根据样本生成扩增和优化深度学习网络的理念提出了一种新的扬声器异常声分类方法。首先,考虑到原始数据特征过于复杂而导致生成样本的质量较差,对扬声器异常声响应信号进行变分模态分解(variational mode decomposition,VMD)突出原始样本的局部特征;其次,从扩增样本角度出发提升模型故障诊断精度,使用最小二乘生成对抗网络(least squares generative adversarial networks,LSGAN)进行对抗训练,生成具有真实样本特征的虚拟样本;最后,选用蝴蝶优化算法(butterfly optimization algorithm,BOA)在大规模权重空间中高效寻优以加速模型收敛,利用深度残差收缩网络(deep residual shrinkage network,DRSN)模型进行扬声器异常声分类,从而提升在样本不均衡情况下的分类准确率及诊断稳定性。实验结果表明:该方法能有效降低误判率,在样本不均衡情况下有效提高故障诊断准确率以及分类诊断的稳定性,其分类平均准确率可达0.9912。 展开更多
关键词 故障诊断 数据不均衡 异常声分类 深度残差收缩网络(DRSN) 蝴蝶优化算法(BOA) 最小二乘生成对抗网络(LSGAN)
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Wind Driven Butterfly Optimization Algorithm with Hybrid Mechanism Avoiding Natural Enemies for Global Optimization and PID Controller Design 认领 引用 被引量：3
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作者 Yang He Yongquan Zhou +2 位作者 Yuanfei Wei Qifang Luo Wu Deng 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第6期2935-2972,共38页
This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabil... This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind states.The algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the wind.Also,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability.Aiming at improving the explorative performance at the initial stages and later stages,the fragrance generation method was modified.To evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions.Further,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020.Finally,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter optimization.Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA. 展开更多
关键词 Butterfly Optimization Algorithm(BOA) Wind Driven Optimization(WDO) Benchmark functions Global optimization Proportional integral derivative(PID) Metaheuristic
Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications 认领 引用
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作者 Selvaraj Vedhanayaki Vairavasundaram Indragandhi 《Global Energy Interconnection》 EI CSCD 2025年第5期891-904,共14页
The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a... The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R2,root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately. 展开更多
关键词 Lithium-ion batteries State of health Machine learning algorithms Bayesian optimization Kernel function
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基于BOA-BiLSTM模型的地表水水质预测 认领 引用 被引量：3
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作者 章佩丽 赵文雅 +1 位作者 许旭敏 包鑫磊 《浙江大学学报（理学版）》 CAS CSCD 北大核心 2025年第3期323-333,345,共11页
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 NH3—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NH3—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
关键词 水质预测 平原河网 贝叶斯优化算法 双向长短期记忆神经网络 现场勘查
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基于BOA-GRU网络的混凝土抗压强度预测方法 认领 引用 被引量：1
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作者 刘立伟 邵海波 +1 位作者 崔凤笠 侯中伟 《广东土木与建筑》 2025年第2期104-107,共4页
抗压强度是混凝土材料设计中的关键力学性能参数,可靠的强度预测可以减少设计成本和时间,并防止因大量配比实验而导致的材料浪费。为此,提出了一种基于门控循环单元(GRU)的混凝土抗压强度预测模型,并采用贝叶斯优化算法(BOA)对GRU的关... 抗压强度是混凝土材料设计中的关键力学性能参数,可靠的强度预测可以减少设计成本和时间,并防止因大量配比实验而导致的材料浪费。为此,提出了一种基于门控循环单元(GRU)的混凝土抗压强度预测模型,并采用贝叶斯优化算法(BOA)对GRU的关键超参数进行优化。收集了1030组混凝土抗压强度数据对BOA-GRU模型进行验证,并与常规的BP神经网络、支持向量回归(SVR)模型的结果进行对比,结果显示BOA-GRU模型的预测精度和可靠性最高,其在均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)等3个指标上均优于对比模型。 展开更多
关键词 混凝土 抗压强度 门控循环单元 贝叶斯优化算法
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