Evolutionary Multi/Many-Objective Optimisation via Bilevel

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

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.

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.

MDOS在气象资料业务中的应用与质量控制措施

自全面实现自动化观测工作以来,以实时传输的数据文件作为质量控制对象在全国范围内得到推广,为气候分析、科研服务、气候论证等提供了有效的数据支撑。本文在概述MDOS系统的基础上,分析了MDOS在气象业务中的应用,同时探讨了常见疑误数据及处理措施,最后给出了质量控制措施。

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.

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。

轻量化高速列车制动盘材料-结构研究进展

400 km/h高速列车紧急制动的强热负荷,导致制动盘温升高、温度分布梯度大和热衰减强,已超出钢质制动盘能载极限,开发耐高温强耐磨的制动盘材料,同时提升制动盘通风散热能力成为制动技术发展瓶颈。综述高速制动盘材料从高强度铸钢和锻钢、轻量化铝基复合材料至耐高温耐磨碳陶复合材料和表面改性技术发展历程,随着碳陶复合材料的台架试验和表面改性技术的摩擦磨损试验研究的深化,表面改性轻质复合材料是制动盘材料未来发展方向。概述通风制动盘内部散热层散热筋结构形貌优化、摩擦层表面耐磨性能改善的发展现状,内部散热筋由直通道设计逐渐演化为弯曲、支柱以及分形通道,镂空结构能更好地加速空气流动提高散热;表面摩擦层引入打孔或划线,更易于摩擦粉尘的抛离从而稳定摩擦因数,保持制动效果;但复杂通风导流结构和打孔划线增加了制备工艺的难度,提出高速制动盘结构构型设计需要综合材料热力学性能、制备工艺及耐热耐磨需求,形成材料-结构-功能—体化设计理念,为实现更高速的制动盘优化设计提供参考。

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。

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。

明清之际川藏道的兴起及在中央和西藏地方中的连接作用

明清之际,川藏道逐渐兴起,并成为中央与西藏地方之间的重要连接纽带。除了中央政府的主动开拓,西藏地方也参与到了整个进程之中。自明中后期开始,大量藏族僧俗人员自川藏道前往内地朝贡,并将巨额茶叶、布匹、金银等物资带回西藏,这是“汉藏金桥”兴起的重要背景。当康区的白利土司阻断了川藏道时,以五世达赖喇嘛为首的西藏地方支持蒙古首领固始汗,攻灭了白利。清初,西藏地方与内地在川藏道东端打箭炉广泛开展贸易活动。康熙三十九年(1700)“西炉之役”与五十九年(1720)清军进藏驱逐准噶尔后,川藏道沿线多数地区逐渐纳入四川省管辖,清朝为保障道路畅通,以巨额茶叶与白银对西藏地方进行经济补贴。由川藏道的兴起及中央与西藏地方的互动,可以深刻理解该道成为双方连接纽带的原因,以及西藏地方牢固纳入中原政治体系的内在逻辑。

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。

河北省科技型中小企业创新生态系统优化路径探讨

以河北省科技型中小企业为对象,探究其发展现状,分析其创新生态系统存在问题,并提出优化对策,有助于推动人才、资本、技术、环境等创新要素的畅通与融合,进一步增强科技型中小企业创新活力,助力经济高质量发展。

水面舰船航行性能MDO系统顶层设计研究

水面舰船航行性能设计是一个典型的多学科设计优化问题,适宜于采用基于系统工程思想的MDO创新设计理念和方法。文章首先分析了水面舰船航行性能设计领域开展MDO应用研究的重要意义,之后阐述了MDO概念内涵;制定了总体研究思路,确定了系统设计目标、技术指标及顶层设计技术方案,剖析了需要首先解决的若干关键技术,并给出关键技术的攻关建议。

基于matlab的飞行器并行MDO方法的研究与实现

在Parmatlab的基础上开发出适合网络通讯的matlab工具箱MDO504 toolbox,此工具箱提供了在matlab环境下进行并行计算的函数。在此工具箱的基础上开发出进行多学科设计优化的软件GSEOPT,GSEOPT在并行环境下实现了基于全局灵敏度方程(GSE)的单级优化算法,用并行的方法求得了全局灵敏度,在系统层进行优化。最后用此软件对某飞行器进行了并行优化设计,给出了计算结果,并与参考文献结果进行了比较,得到了更好的结果。

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。

导弹总体参数优化设计的合作协同进化MDO算法

传统的导弹设计方法不能很好地实现各子系统设计的协同 ,而多学科设计优化技术十分适合现代计算机网络环境下的多学科协同设计。根据协同进化与MDO在本质上的相似性 ,采用合作协同进化的方法进行MDO算法研究 ,以充分发挥进化算法的优越性。给出了一种基于合作协同进化的MDO算法 ,将该算法应用于导弹的气动 发动机 控制一体化优化设计。

基于MDO技术的油底壳结构优化方法研究

为提高油底壳的结构刚度,减轻质量,研究探讨了一种利用成熟CAD/CAE软件和多学科设计优化(MDO)平台进行内燃机部件结构优化设计的方法。利用iSIGHT、I-DEAS软件和VC++语言,构建了一套结构优化集成系统,实现了基于MDO技术的油底壳结构优化的整个仿真流程,并对程序控制的实体模型参数化修改、集成框架以及相关优化算法等进行了探讨。针对某型号柴油机油底壳,利用连续二次规划(NLPQL)算法,优化求得了结构参数全局最优解。实践结果表明:MDO技术应用于内燃机结构优化设计,是实用和可行的。