A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

Exploration of the Training Mode of School-enterprise Collaborative AI Innovative Talents Based on PaddlePaddle

This paper takes the school-enterprise cooperation between the University of Electronic Science and Technology of China(UESTC)and Baidu(China)Co.,Ltd.as an example to build the Paddle Paddle(Sichuan)AI Education Innovation Center by enjoying the best of both UESTC and Baidu(China),and cooperating with 16 universities in Sichuan Province.With the support of this center,both the school and the enterprise successfully built a school-enterprise collaborative AI innovative talent training model,which mainly serves the universities,industries,and districts.Furthermore,this training model is able to facilitate the update of the industrial intelligence and the regional economic development in southwest China,and also provide a reference for deep integration of school-enterprise collaboration and the cultivation of innovative talents in electronic information fields.

Multi-Objective Optimization of Aluminum Alloy Electric Bus Frame Connectors for Enhanced Durability

The widespread adoption of aluminumalloy electric buses,known for their energy efficiency and eco-friendliness,faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel.This issue is further exacerbated by the stringent requirements imposed by the flammability and explosiveness of batteries,necessitating robust frame protection.Our study aims to optimize the connectors of aluminum alloy bus frames,emphasizing durability,energy efficiency,and safety.This research delves into Multi-Objective Coordinated Optimization(MCO)techniques for lightweight design in aluminum alloy bus body connectors.Our goal is to enhance lightweighting,reinforce energy absorption,and improve deformation resistance in connector components.Three typical aluminum alloy connectors were selected and a design optimization platform was built for their MCO using a variety of software and methods.Firstly,through three-point bending experiments and finite element analysis on three types of connector components,we identified optimized design parameters based on deformation patterns.Then,employing Optimal Latin hypercube design(OLHD),parametric modeling,and neural network approximation,we developed high-precision approximate models for the design parameters of each connector component,targeting energy absorption,mass,and logarithmic strain.Lastly,utilizing the Archive-based Micro Genetic Algorithm(AMGA),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-dominated SortingGenetic Algorithm(NSGA2),we explored optimized design solutions for these joint components.Subsequently,we simulated joint assembly buckling during bus rollover crash scenarios to verify and analyze the optimized solutions in three-point bending simulations.Each joint component showcased a remarkable 30%–40%mass reduction while boosting energy absorption.Our design optimization method exhibits high efficiency and costeffectiveness.Leveraging contemporary automation technology,the design optimization platform developed in this study is poised to facilitate intelligent optimization of lightweight metal components in future applications.

Research Progress of Aerodynamic Multi-Objective Optimization on High-Speed Train Nose Shape

The aerodynamic optimization design of high-speed trains(HSTs)is crucial for energy conservation,environmental preservation,operational safety,and speeding up.This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs.First,the study explores the impact of train nose shape parameters on aerodynamic performance.The parameterization methods involved in the aerodynamic multiobjective optimization ofHSTs are summarized and classified as shape-based and disturbance-based parameterizationmethods.Meanwhile,the advantages and limitations of each parameterizationmethod,aswell as the applicable scope,are briefly discussed.In addition,the NSGA-II algorithm,particle swarm optimization algorithm,standard genetic algorithm,and other commonly used multi-objective optimization algorithms and the improvements in the field of aerodynamic optimization for HSTs are summarized.Second,this study investigates the aerodynamic multi-objective optimization technology for HSTs using the surrogate model,focusing on the Kriging surrogate models,neural network,and support vector regression.Moreover,the construction methods of surrogate models are summarized,and the influence of different sample infill criteria on the efficiency ofmulti-objective optimization is analyzed.Meanwhile,advanced aerodynamic optimization methods in the field of aircraft have been briefly introduced to guide research on the aerodynamic optimization of HSTs.Finally,based on the summary of the research progress of the aerodynamicmulti-objective optimization ofHSTs,future research directions are proposed,such as intelligent recognition technology of characteristic parameters,collaborative optimization of multiple operating environments,and sample infill criterion of the surrogate model.

Current Situation and Mode of Talent Training in Marine Cultural Industry Based on Collaborative Innovation of Governments,Enterprises,Colleges,Scientific Institutions and Users

Based on the present situation of talent training in marine cultural industry,the collaborative innovation mechanism of governments,enterprises,colleges,scientific institutions and users was used to construct the mode of talent training in marine cultural industry. It is needed to give full play to the active roles of governments,enterprises,colleges,scientific institutions and users in the training of marine cultural talents,develop training mode of innovative scientific and technological talents,speed up the construction of collaborative innovation ability of marine cultural talents,and improve the efficiency of talent training.

Choice between Ways of Talent Training in Marine Cultural Industry--Based on Collaborative Innovation of Governments, Enterprises, Colleges, Scientific Institutions and Users

Based on the present situation of talent training in marine cultural industry,the collaborative innovation mode of governments,enterprises,colleges,scientific institutions and users was used to study the ways of talent training in marine cultural industry.

Study on Talent Training Mechanisms of Marine Cultural Industry Based on Collaborative Innovation of Governments,Enterprises,Colleges,Scientific Institutions and Users

Based on the review of related concepts,the current situation and problems of talent training in marine cultural industry in China were analyzed,and talent training mechanisms of marine cultural industry based on collaborative innovation of governments,enterprises,colleges,scientific institutions and users were proposed to promote the transformation,upgrading and vigorous development of marine cultural industry,accelerate the spread of marine culture with Chinese characteristics,and build China into a maritime power.

Research on Talent Training System of Marine Cultural Industry Based on Collaborative Innovation of Governments,Enterprises,Colleges,Scientific Institutions and Users

This paper focuses on the coupling between talent training system of marine cultural industry and collaborative innovation of govern-ments, enterprises, colleges, scientific institutions and users. Firstly, the significance of talent training in marine cultural industry for the construction of China as a maritime power was analyzed. Secondly, the current situation of talent training in China＇s marine cultural industry and the existing problems were analyzed. Finally, how to integrate the collaborative innovation of governments, enterprises, colleges, scientific institutions and us-ers into the talent training system of marine cultural industry was explored to help talent training to break through the bottleneck and promote the vig-orous development of marine cultural industry.

Application of Collaborative Innovation of Governments,Enterprises,Colleges,Scientific Institutions and Users in the Talent Training of Marine Cultural Industry: A Case Study of Maritime Universities

Based on the literature review of collaborative innovation of governments, enterprises, colleges, scientific institutions and users, the ap-plication situation of collaborative innovation of governments, enterprises, colleges, scientific institutions and users in the talent training of marine cultural industry in maritime universities was analyzed, and the problems existing in the talent training of marine cultural industry were explored. Finally, corresponding countermeasures and suggestions were put forward.

Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

Detecting fake reviewers in heterogeneous networks of buyers and sellers:a collaborative training-based spammer group algorithm

It is not uncommon for malicious sellers to collude with fake reviewers(also called spammers)to write fake reviews for multiple products to either demote competitors or promote their products'reputations,forming a gray industry chain.To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers,researchers have conducted extensive research using Frequent Item Mining-based and graph-based meth-ods.However,these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features,and structure-attribute correlation,resulting in poorer detection performance.There-fore,we propose a collaborative training-based spammer group detection algorithm by constructing a heterogene-ous induced sub-network based on the target product set to detect cross-product attack spammer groups.To jointly consider all available features,we use the collaborative training method to learn the feature representations of nodes.In addition,we use the DBSCAN clustering method to generate candidate groups,exclude innocent ones,and rank them to obtain spammer groups.The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.

Multi-objective aerodynamic optimization design of high-speed maglev train nose

Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence,the study analyzes aerodynamic parameters with multi-objective optimization design.Design/methodology/approach–The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics.Then the modified vehicle modeling function(VMF)parameterization method and surface discretization method are adopted for the parametric design of the nose.For the 12 key design parameters extracted,combined with computational fluid dynamics(CFD),support vector machine(SVR)model and multi-objective particle swarm optimization(MPSO)algorithm,the multi-objective aerodynamic optimization design of highspeed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint.The engineering improvement and wind tunnel test verification of the optimized shape are done.Findings–Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train.The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.Originality/value–Compared with the original shape,the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%,and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%,respectively,after adopting the optimized shape modified according to engineering design requirements.

A multi-screen collaboration-based low-cost portable dry-lab simulator for basic laparoscopic skills training: A technical note

Laparoscopic skills training has always been crucial for novice surgeons. Readily accessible equipment, aswell as structured training curriculum should be provided to guarantee adequate practice hours and skillproficiency. Dry-lab training is typically adopted before animal model surgery, usually comprising ofpurpose-built bulky simulators that is neither accessible nor portable. In this technical note, we designed ahome-made simulator, using two 4 L water jugs as operating space that are communicated inside, plus anobservation hole taped in between to mimic the triangular working space of laparoscopic surgery. Imagingwas achieved via smartphone camera, which was wirelessly connected to a laptop and a projector for realtime display on multiple screens, using built-in multi-screen collaboration software. A self-regulated andproficiency-based training curriculum was adopted. This dry-lab simulator is low-cost, highly portable andeasily replicable for basic laparoscopic skills training for the beginners to intermediate surgeons, whichmay serve as a good way for the standardized residency and specialist training program.

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation.In order to improve the oper-ating energy utilization rate of trains,a multi-objective particle swarm optimiza-tion(MPSO)algorithm with energy consumption,punctuality and parking accuracy as the objective and safety as the constraint is built.To accelerate its the convergence process,the train operation progression is divided into several modes according to the train speed-distance curve.A human-computer interactive particle swarm optimization algorithm is proposed,which presents the optimized results after a certain number of iterations to the decision maker,and the satisfac-tory outcomes can be obtained after a limited number of adjustments.The multi-objective particle swarm optimization(MPSO)algorithm is used to optimize the train operation process.An algorithm based on the important relationship between the objective and the preference information of the given reference points is sug-gested to overcome the shortcomings of the existing algorithms.These methods significantly increase the computational complexity and convergence of the algo-rithm.An adaptive fuzzy logic system that can simultaneously utilize experience information andfield data information is proposed to adjust the consequences of off-line optimization in real time,thereby eliminating the influence of uncertainty on train operation.After optimization and adjustment,the whole running time has been increased by 0.5 s,the energy consumption has been reduced by 12%,the parking accuracy has been increased by 8%,and the comprehensive performance has been enhanced.

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

Training System of Innovative and Entrepreneurial Talents based on University-Enterprise Collaboration

With the continuous development of China's education,the diversification of personnel training has gradually become an important part of China's social development and construction.As a new teaching mode in higher education,the university-enterprise collaborative training mode is an important guarantee for training high-quality,compound and practical professionals.Based on this,first of all,the current situation and problems of the training mode of innovative and entrepreneurial talents based on university-enterprise collaboration and its causes were expounded;then a new innovative and entrepreneurial talents training system based on university-enterprise collaboration was constructed from four aspects of education,practice,training and guidance;finally,the guarantee mechanism of the system was proposed from the aspects of culture,system and law,so as to promote the development of education in China.

基于Co-Training的微博垃圾评论识别方法

微博上大量的垃圾评论对个人、社会,甚至是对国家都会造成不良影响。为对微博中的垃圾评论进行识别,提出基于协同训练的微博垃圾评论识别方法。定义一种基于规则的识别方法过滤出显式垃圾评论,剩余的评论归为相关评论,构建AdaBoost分类器和支持向量机分类器,通过Co-Training算法进行协同训练,判断其是否为垃圾评论,以提高分类精度,节省样本标注工作。实验结果表明,与基于相似度计算的垃圾评论识别方法、基于评论多特征的垃圾评论识别方法相比,该方法具有较好的识别效果。

半监督学习的Co-training算法研究

介绍一种基于半监督学习的协同训练(Co-training)分类算法,当可用的训练样本比较少时,使用传统的方法进行分类,如决策树分类,将无法得到用户满意的结果,而且它们需要大量的标记样本。事实上,获取有标签的样本的代价是相当昂贵的。于是,使用较少的已标记样本和大量的无标记样本进行协同训练的半监督学习,成为研究者首选。

Novel Models and Algorithms of Load Balancing for Variable-structured Collaborative Simulation under HLA/RTI

High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.