Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Numerical optimisation of a classical stochastic system for targeted energy transfer

The paper studies stochastic dynamics of a two-degree-of-freedom system,where a primary linear system is connected to a nonlinear energy sink with cubic stiffness nonlinearity and viscous damping.While the primary mass is subjected to a zero-mean Gaussian white noise excitation,the main objective of this study is to maximise the efficiency of the targeted energy transfer in the system.A surrogate optimisation algorithm is proposed for this purpose and adopted for the stochastic framework.The optimisations are conducted separately for the nonlinear stiffness coefficient alone as well as for both the nonlinear stiffness and damping coefficients together.Three different optimisation cost functions,based on either energy of the system’s components or the dissipated energy,are considered.The results demonstrate some clear trends in values of the nonlinear energy sink coefficients and show the effect of different cost functions on the optimal values of the nonlinear system’s coefficients.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

社会责任视角下的纺织印染工艺优化与实践

随着全球经济的快速发展,纺织印染行业作为资源密集型行业,面临着巨大的社会责任挑战。在环境污染、能源消耗、资源管理以及劳动力和社会福祉方面,纺织印染行业亟需寻找一种符合可持续发展原则,实现经济效益的优化途径。在社会责任视角下,优化纺织印染工艺,既要求技术创新,也要求在生产全过程中实施社会责任。这种纺织印染工艺的优化与实践可以为纺织印染行业提供一条实现环境可持续和社会责任兼顾的优化路径。

少数民族大学生中华民族共同体意识培育路径探析

新的历史时代,以民族团结为主题的各民族共同发展已经成为中国式现代化建设的重要内容。加强少数民族大学生中华民族共同体意识建设,就要以辩证唯物主义和历史唯物主义的马克思主义科学理论为指导思想,以习近平总书记关于加强和改进民族工作重要思想的立场、观点和方法为培育抓手,发挥各方合力,共同促进少数民族大学生的中华民族共同体意识教育。具体应着力抓好以下几方面的工作:一要加大少数民族大学生马克思主义民族团结理论教育;二要促进少数民族大学生对习近平新时代中国特色社会主义思想关于民族论述的正确理解;三要加强对少数民族大学生社会主义核心价值体系的培育;四要落实“石榴红”项目建设,培养少数民族大学生干部队伍。最终,使少数民族学生真正成为拥护中国共产党领导、认同社会主义核心价值观的有为之士。

非冷链商品配送路径优化研究--以京东配送为例

如何有效降低商品配送成本是物流企业的关注重点,学界已就带时间窗的商品配送路径优化算法展开了相关研究,但相关算法还存在着过早陷入局部最优或无法收敛的问题。由此提出一种改进的变邻域遗传搜索算法(VNS-GA),以非冷链商品配送为研究对象,构造起求解物流配送车辆路径规划的数学模型。首先,以配送成本和缺货惩罚成本的最小化作为实现目标,构建了包括车辆使用成本、配送运输成本和时间窗口惩罚成本的配送路径优化模型;其次,运用变邻域遗传优化算法来实现多目标物流配送路径的优化;最后,以京东某北京配送中心的物流配送为例,运用MATLAB软件对VNS-GA算法模型的科学性及有效性进行仿真验证。经实证,VNS-GA算法与传统算法相比具有更好的全局和局部搜索能力。研究期望为配送车辆调度与配送路径规划提供更优的路径选择模型,从而降低物流配送成本和减少便利店因缺货造成的损失。

基于文献计量的建筑碳排放评估进展与分析

基于Citespace软件,利用文献计量法,对2011年1月到2022年2月期间的建筑碳排放评估的关键成果以及关键词共现分析和共被引分析的结果进行研究,分析碳排放评估的研究热点,结果得出未来的研究重点应关注建筑碳排放评估优化以及混合方法的研究。其中,建立统一的建筑碳排放数据库和开展碳排放不确定性分析是影响评估精度的关键。

基于改进PSO-BP神经网络的热采管柱应力预测

稠油热采过程中,油套管柱由于在温度、地层等多重载荷作用下发生塑性形变进而导致断裂或失效。文中根据热采管柱高温服役工况,引入异步变化学习因子和自适应权重建立输入参数为注汽温度、井深、非均匀系数和水泥环温度,输出参数为套管应力的改进PSO-BP模型。文中以N80热采套管为例,选取260、280、300、320、340℃5种温度工况下有限元模拟结果作为训练数据,对比BP模型、GA-BP模型、MEA-BP模型、PSO-BP模型和改进PSO-BP模型在300℃工况温度下井深200、300、400、500、600、700 m处套管应力的预测值和试验值、有限元计算值。结果表明:改进PSO-BP模型预测的应力与试验值最接近,最大和最小误差分别为2.69%和0.06%。最后从训练数据、预测误差、计算时间等方面对建立的改进PSO-BP模型进行了评价,为热采管柱服役过程中的强度安全分析提供智能高效的模型。

Multiresolution Isogeometric Topology Optimisation Using Moving Morphable Voids

A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.

发酵乳制品风味形成机制及优化控制研究进展

发酵乳制品营养丰富,深受广大消费者喜爱。风味是发酵乳制品最重要的感官属性之一,但乳制品在发酵过程中极易产生不良风味,影响发酵乳制品的风味品质,因此充分了解发酵乳制品中风味物质的转化过程及形成机制对优化风味有重要意义。基于相关研究,综合论述了发酵乳制品风味形成途径的重要研究进展,阐述了风味优化控制的重要技术措施。通过对相关文献的梳理,指出发酵乳制品风味形成过程中碳水化合物代谢、脂肪分解、蛋白质水解等关键生化转化途径,以及通过靶向筛选和鉴定产香乳酸菌、优化加工条件、应用基因工程等优化风味的方法。未来可就乳酸菌的开发及在调控风味生成的潜在代谢机制等方面展开深入研究。

Optimisation-based Verification Process of Obstacle Avoidance Systems for Unicycle-like Mobile Robots

This paper presents an optimisatiombased verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated： genetic algorithms （GAs） and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.

汽轮机中压联合汽阀优化设计

针对某型汽轮机中压联合汽阀压损较高的问题,提出了一种基于多孔介质流动模型的计算方法,采用多孔介质模型模拟阀门中的滤网,对不同优化方案的汽阀进行了稳态计算流体力学模拟,对比分析了各优化方案对中压阀压损的影响。研究表明:相较于改变调节阀远端与阀杆中轴线距离的方案,对调节阀圆角处进行曲率优化可以有效降低汽阀压损,且对几何改动较小,效果更为理想。

Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

优化光谱指数结合PLSR的多金属矿区土壤As含量高光谱反演

砷(As)是我国多金属矿区的主要污染物之一,对环境、农业和人类健康构成严重威胁。近地高光谱技术具有快速、动态、无损、光谱分辨率高等优势,对于多金属矿区土壤As污染监测与综合治理具有巨大应用潜力。然而,由于受污染区域、土壤背景以及高光谱质量、光谱输入量等因素影响,高光谱反演模型的适用性和精度差异较大。本研究针对湘南某多金属矿区,基于Pearson相关性分析并结合变量投影重要性(VIP)准则,提取18种变换光谱形式下的单变量特征波段及4种光谱指数算法下的优化光谱指数作为光谱输入量,建立偏最小二乘回归(PLSR)模型,实现了矿区土壤As含量反演。结果表明:倒数(RT)、对数(L)、平方根(Sqrt)、标准正态变量变换二阶导(SNV_SD)等变换后的光谱数据与As含量具有较高的相关性;优化光谱指数能从二维光谱空间揭示As的光谱响应,相较于单变量特征波段,以优化光谱指数为自变量构建的模型性能更优;比值指数(RI)模型的R_(c)^(2)、RMSE_(c)、R_(p)^(2)、RMSE_(p)、RPD分别为0.908、50.8 mg/kg、0.949、35.6 mg/kg、4.45,是研究区土壤As含量反演的最优模型。单变量特征波段结合优化光谱指数预测土壤As含量具有较好的可行性,可为多金属矿区土壤As污染高光谱快速监测提供科学依据。

大型矿用挖掘机履带板故障分析和优化设计

依据现场履带板失效形式,通过理论分析、有限元分析等方法和手段,对履带板现有结构进行改进设计;通过有限元对比分析验证新结构受力状态更好;提供了一种解决现场问题的思路,为以后产品设计和解决现场问题提供理论依据。

面向脱轨后被动安全防护的转向架结构优化

为了验证和优化列车转向架部件的脱轨后被动安全防护效果,提出基于多边形接触模型的列车脱轨后接触建模方法,并建立列车脱轨后动力学模型,模拟不同结构参数组合下的列车脱轨后运行行为。将所得横移量和结构参数组合作为反向传播神经网络模型的训练样本,拟合两者之间的非线性关系,采用基于锦标赛选择的自适应遗传算法搜索横移量最小解及其对应的参数组合。以拖车制动盘优化问题为例,运用该优化模型进行结构参数优化。研究结果表明:在100 km/h脱轨速度下,制动盘的最优结构参数组合的半径为335 mm、厚度为80 mm、安装位置为320 mm;与原设计相比,车辆脱轨后横移量减少80.2%,大幅度提高了制动盘被动安全防护能力。本文所提出的建模方法和优化模型可推广应用于其他列车结构的脱轨后被动安全防护能力优化和验证,具有较大的工程实用价值。

基于多目标优化及成本效益分析的古城镇微型消防站布局研究

古城镇含有大量火灾风险较大的木质建筑,微型消防站被普遍应用于古城镇消防。不同于传统的消防站选址问题,古城镇微型消防站布局的难点在于要考虑路网的连通性,同时在有限投资下找到最佳消防站点布局(包括位置和数量)。针对该问题,本文提出基于多目标优化及成本效益分析的古城镇微型消防站布局研究,首先绘制古城镇道路无向图,使用Dijkstra算法计算最优路径得出消防站点可覆盖矩阵;然后以3 min路网可达性作为约束条件,构建最大覆盖率和最小消防站点数为目标的优化问题,通过非支配遗传算法求解最优消防站点位置集;最后通过分析成本与数量之间的关系,确定最佳消防站点数量及布局。以中国西南地区的一个古镇为例进行说明,在设置7个消防站点的条件下,本文给出的布局方案与现役方案相比减少了一个站点,而覆盖率增长7.5%。结果证明:所提出的方法能够有效确认经济性最佳、覆盖范围最大的消防站位置集。

三自由度精密检测平台结构设计及Workbench响应面优化

孔径测量仪是测量环规直径或量块端度的常用仪器,三自由度精密检测平台是孔径测量仪机械部分的核心,可以稳定支撑待测物,并能进行手动独立调节,满足孔径测量要求。对三自由度精密检测平台进行结构设计,使其满足y向平移、M_(z)偏转、M_(y)倾斜三个自由度的调节。从SolidWorks建立的三自由度精密检测平台装配体中取出底层板三维模型,化简后导入ANSYS Workbench中进行静力学分析,得到其变形云图和应力云图。为降低平台的变形量,在ANSYS中对底层版进行响应面优化分析。响应面优化分析选用中心复合实验设计,设计变量为底板厚度和三个弹簧立柱的支撑力,响应为变形量,得到25组实验点。根据实验结果,利用Kriging插值法建立了底板的响应面模型,并对各相关设计变量进行了敏感度分析。使用多目标遗传算法完成了底板的优化设计,与原始设计的结构方案相比,平台的变形量减少20.1%。