Target distribution in cooperative combat based on Bayesian optimization algorithm

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.

Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

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.

Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion

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.

Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

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.

Learning Bayesian network structure with immune algorithm

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.

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm（CBOA） is proposed according to the characteristics of the function independence,conformity and supplementary between the electronic countermeasure（ECM） and the firepower attack systems.The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome.Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations,but also maintain the population multiplicity,and enhance the algorithm performance.The simulation result confirms the algorithm validity.

BOA-NCF-AW混合算法在光伏最大功率跟踪的运用

在局部遮荫下,针对传统最大功率跟踪MPPT(maximum power point tracking)算法不能跳出局部最优找到全局最大功率,及传统蝴蝶优化算法BOA(butterfly optimization algorithm)存在搜索震荡大和收敛慢等问题,提出一种新型的MPPT控制算法。该算法在传统蝴蝶算法上加入收敛因子,来加快全局搜索速度;引入自适应权重系数,来提高蝴蝶优化算法在局部搜索的搜索速度及追踪精度等性能。通过仿真,对比混合算法(INBOA)与BOA、粒子群优化PSO(particle swarm optimization)算法、灰狼优化算法GWO(gray wolf optimization)的函数收敛曲线,验证所提算法具有收敛速度快、搜索精度高的优点;对比INBOA、BOA、PSO、GWO的MPPT算法在静态与动态环境下的性能指标可知,INBOA的MPPT算法具有更高追踪效率、更快收敛速度以及更小的搜索震荡。从而进一步验证混合算法的优越性。

基于注意力机制与XBOA-Bi-LSTM的离心式压缩机故障预警方法

由于离心式压缩机存在着运行工况复杂、维修成本昂贵和长输管道工作环境恶劣的问题,为此,提出了一种基于注意力机制(AM)和蝴蝶算法优化双向长短期记忆神经网络(XBOA-Bi-LSTM)的离心式压缩机故障预警方法。首先,针对传统蝴蝶算法的收敛速度慢、转换概率单一和容易陷入局部最优等问题,通过引入无限折叠迭代混叠映射以丰富蝴蝶算法的初始种群;同时,提出了一种基于种群离散度与迭代次数的自适应惯性转换概率,以提高蝴蝶算法的寻优能力;然后,采用了灰色关联度分析法对测点数据进行了特征提取,结合注意力机制对输入序列进行了灰色关联度系数赋权;最后,建立了双向长短期记忆神经网络故障预警模型,采用仿真实验完成了对离心式压缩机的故障预警;以某天然气长输管道机组的离心式压缩机作为仿真对象,对该离心式压缩机故障预警方法的可行性进行了验证。研究结果表明:采用基于注意力机制与XBOA-Bi-LSTM的离心式压缩机故障预警方法时,在离心式压缩机故障发生前2 h~3 h内就发出预警信号,实现了对于离心式压缩机进气过滤器压差异常与支撑轴承工作异常的故障预警目的。

结合局部结构学习的Bayesian优化算法

在Bayesian优化算法中Bayesian网络的学习是算法应用的关键,而Bayesian网络学习是一个NP-hard问题,并且计算量大。为了能够快速获得较稳定的Bayesian网络,提出了一种新的学习策略,在学习Bayes-ian网络结构时采用对局部结构的贪婪算法,并结合局部搜索利用打分测度选取最优边。对所提算法进行了分析,在算法复杂度较小的情况下,所学习的Bayesian网络可靠性明显提高,算法收敛速度加快,并且避免陷入局部最优。仿真研究表明文章所提出算法寻优能力优于传统Bayesian优化算法。

基于变异的Bayesian优化算法

将变异算子与Bayesian优化算法相结合,提出了一种基于变异的Bayesian优化算法。在算法中设计了一个种群多样性函数,通过此函数引入变异算子,目的是利用变异算子的邻域搜索能力,保持种群多样性,将贝叶斯概率模型提取的全局信息与变异算子的局部信息联系起来,避免陷入局部最优。仿真研究表明基于变异的Bayesian优化算法的寻优能力比Bayesian优化算法更强。

基于Bayesian优化算法的防区外导弹低空突防路径规划

贝叶斯优化算法能够通过贝叶斯网络来估计候选解的节点联合分布并且用该分布来产生新的候选解,该过程反复迭代就可以求得问题的最优解。仿真实例利用基于决策图的贝叶斯优化算法(BOA)得到了防区外导弹飞行的最佳路径.证明了 BOA 是解决该类优化问题的简单、有效的进化方法.

基于BOA-ELM的区域VOCs质量浓度空间插值方法研究

针对目前挥发性有机物(Volatile Organic Compounds,VOCs)质量浓度插值方法单一且插值精度不高的问题,为了提升区域内VOCs质量浓度空间插值的精度,构建BOA-ELM(Butterfly Optimization Algorithm-Extreme Learning Machine)空间插值模型,首次将神经网络模型应用于VOCs质量浓度空间插值。首先对研究区域进行网格划分,其次利用BOA-ELM进行插值研究,同时讨论气象特征对空间插值的重要性,最后将VOCs质量浓度空间插值结果可视化。以陕西省为例,对VOCs质量浓度进行插值,结果显示:加入气象特征变量能提高模型插值精度,且与原始极限学习机(Extreme Learning Machine,ELM)模型以及传统插值方法反向离权重法(Inverse Distance Weighted,IDW)对比,显示BOA-ELM模型的均方根误差(ZRMSE)、平均绝对误差(ZMAE)、平均绝对百分比误差(ZMAPE)均为最小值,分别为8.69μg/m^(3)、6.72μg/m^(3)、7.10%,优于IDW模型。结果表明BOA-ELM模型能很好地应用VOCs质量浓度空间插值,为大气污染物的空间插值提供了新的思路与方法。

结合先验知识的Bayesian优化算法研究与仿真

由于一般优化问题的先验知识很难获取,因此在Bayesian网络学习中结合与利用先验知识一直是一个很难突破的问题。针对Bayesian优化算法(BOA)的特点,对一般优化问题如何发现和利用先验知识进行了分析讨论,把BOA中前一代种群所提供的信息作为先验知识结合到当前代Bayesian网络的学习中,提高了所学习网络的可靠性,从而提高算法的性能。仿真结果表明所提算法比传统BOA具有更强的全局寻优能力。

基于ICEEMDAN和IMWPE-LDA-BOA-SVM的齿轮箱损伤识别模型

针对齿轮箱振动信号中的背景噪声过大影响故障特征质量,进而降低故障识别准确率的问题,提出了一种基于改进自适应噪声完备集成经验模态分解(ICEEMDAN)、改进多尺度加权排列熵(IMWPE)、利用线性判别分析(LDA)、蝴蝶优化算法(BOA)优化支持向量机(SVM)的齿轮箱故障诊断方法(ICEEMDAN-IMWPE-LDA-BOA-SVM)。首先,采用ICEEMDAN对齿轮箱振动信号进行了分解,生成了一系列从低频到高频分布的本征模态函数分量;接着,基于相关系数筛选出包含主要故障信息的本征模态函数分量,进行了信号重构,降低了信号的噪声;随后,提出了改进多尺度加权排列熵的非线性动力学指标,并利用其提取了重构信号的故障特征,以构建反映齿轮箱故障特性的故障特征;然后,利用线性判别分析(LDA)对原始故障特征进行了压缩,以构建低维的故障特征向量;最后,采用蝴蝶优化算法(BOA)对支持向量机(SVM)的惩罚系数和核函数参数进行了优化,以构建参数最优的故障分类器,对齿轮箱的故障进行了识别;基于齿轮箱复合故障数据集对ICEEMDAN-IMWPE-BOA-SVM方法进行了实验和对比分析。研究结果表明:该方法能够较为准确地识别齿轮箱的不同故障类型,准确率达到了99.33%,诊断时间只需5.31 s,在多个方面都优于其他对比方法,在齿轮箱的故障诊断中更具有应用潜力。

基于保留策略的Bayesian网优化算法

提出了一种基于保留策略的Bayesian网优化算法。算法中通过学习Bayesian网络自动获取进化过程中各基因之间的依赖关系及分布描述,以便更好地指导算法的进化,并利用保留的父辈中间群体扩充学习数据集规模,解决了Bayesian网学习可靠性与较大群体规模之间的矛盾。实验表明,算法能够在有效收敛的前提下降低对群体规模的要求,具有较高的学习效率。

动态扰动下高心墙堆石坝无人碾压BOA-PID循迹控制

无人碾压机精准循迹控制对确保压实质量意义重大。然而,高心墙堆石坝具有坝料粒径分布广且松铺厚度不均匀等特征,导致坝面不平整且异质性强,对无人碾压机循迹控制造成动态扰动。传统的比例积分微分(proportion integral derivative,PID)控制算法采用固定参数进行纠偏控制,难以快速纠正动态扰动导致的循迹偏差。针对上述问题,本文以蝴蝶优化算法(butterfly optimization algorithm,BOA)动态优化PID控制参数,提出BOA-PID无人碾压机循迹控制方法。首先,构建车身倾斜模型以修正坝面不平整条件下无人碾压机的定位误差;其次,基于运动学模型动态预测无人碾压机的纠偏距离;再者,以最小化纠偏距离为目标函数,采用BOA动态优化PID算法的比例、积分和微分参数;最后,以参数优化的PID计算无人碾压机的转向控制量,从而克服动态扰动,实现高心墙堆石坝复杂条件下的快速纠偏。本文结合中国西南两河口大型水利水电工程开展仿真与实地实验,以验证所提出方法的有效性和先进性。结果表明,BOA-PID的纠偏能力优于GA-PID(genetic algorithm)、PSO-PID(particle swarm optimization)、DA-PID(dragonfly algorithm)和传统PID,且所提出方法能够实现高心墙堆石坝复杂条件下无人碾压机的精准循迹控制,堆石料(4.44 cm)和心墙料(3.32 cm)碾压的平均循迹误差均小于5 cm。

一种基于BOA-SAE-EELM的光伏阵列故障诊断方法

光伏阵列非线性输出的特性以及最大功率点跟踪算法,会影响光伏阵列保护设备的工作。为了正确辨识光伏阵列的运行状态,本研究提出一种基于贝叶斯优化算法(BOA)、堆栈自动编码器(SAE)以及集成极限学习机(EELM)相结合的故障诊断方法。首先,将光伏阵列的时序波形进行标准化处理;接着,使用SAE对标准化后的时序波形进行特征自动提取,并训练一个EELM的故障分类模型;最后,利用BOA对诊断模型的超参数进行优化。实验结果表明所提方法对仿真和实验的故障诊断准确率分别达到了98.40%和98.10%,优于反向传播(BP)神经网络、支持向量机、随机森林等方法。

基于VMD与Bayesian-LSSVM的滚动轴承故障诊断方法

提出一种变分模态分解(VMD)与贝叶斯优化的最小二乘支持向量机(Bayesian-LSSVM)相结合的滚动轴承故障诊断方法。该方法利用变分模态分解对滚动轴承的振动信号进行分解,得到一系列的固有模态函数和特征向量,然后采用最小二乘支持向量机对故障类型进行分类识别,并利用贝叶斯算法对最小二乘支持向量机的参数进行优化。实验结果表明:基于VMD与Bayesian-LSSVM的方法在故障类型模式识别上具有较高的准确率。

Air Combat Assignment Problem Based on Bayesian Optimization Algorithm

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.

基于BOA-ELM的混凝土抗压强度预测研究

为控制控制混凝土生产成本,在混凝土拌和期限制抗压强度不足的缺陷构建产出,可以有效降低原料的浪费,是节能降耗的关键方法之一。针对混凝土抗压强度的传统测量方法严重滞后的问题,提出了基于贝叶斯优化极限学习机(BOA-ELM)的混凝土抗压强度预测方法。首先,分析了混凝土拌和过程中对抗压强度预测值实时获得的需求。以各物料的用量为分析基础,28天标准养护后混凝土抗压强度值为预测目标,设计了基于极限学习机的强度预测模型。其次,为进一步提高模型的稳定性以及准确行,提出基于贝叶斯优化的极限学习机模型,根据模型超参数的分布特征,以高斯过程作为超参的先验分布,预测误差最小化作为目标,寻找最优的模型超参。最后,在实际施工产生的C50标号混凝土数据集上测试文中模型,并对比分析了其他预测模型和寻优算法。结果表明,结合了贝叶斯优化的极限学习机预测模型相较于经典算法具有更高的预测准确性和模型训练的高效性。