In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based bou...In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.展开更多
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t...Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.展开更多
When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturin...When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.展开更多
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t...Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-...The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.展开更多
A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material d...A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to ob- tain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.展开更多
Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collabora...Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.展开更多
To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the ...To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.展开更多
This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequentia...This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.展开更多
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 ...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.展开更多
Both the seat and cab system of truck play a vital role in ride comfort.The damping matching methods of the two systems are studied separately at present.However,the driver,seat,and cab system are one inseparable whol...Both the seat and cab system of truck play a vital role in ride comfort.The damping matching methods of the two systems are studied separately at present.However,the driver,seat,and cab system are one inseparable whole.In order to further improve ride comfort,the seat suspension is regarded as the fifth suspension of the cab,a new idea of ＂Five-suspensions＂ is proposed.Based on this idea,a 4 degree-of-freedom driver-seat-cab coupled system model is presented.Using the tested cab suspensions excitations as inputs and seat acceleration response as compared output,the simulation model is built.Taking optimal ride comfort as target,a new method of damping collaborative optimization for Five-suspensions is proposed.With a practical example of seat and cab system,the damping parameters are optimized and validated by simulation and bench test.The results show the seat vertical frequency-weighted RMS acceleration values tested for the un-optimized and optimized Five-suspensions are 0.50 m/s～2 and 0.39 m/s～2,respectively,with a decrease by 22.0%,which proves the model and method proposed are correct and reliable.The idea of ＂Five-suspensions＂ and the method proposed provide a reference for achieving global optimal damping matching of seat suspension and cab suspensions.展开更多
China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of ne...China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.展开更多
Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the ...Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.展开更多
Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative partic...Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.展开更多
Based on multidisciplinary design optimization（MDO）,a new design method is put forward for hydraulic shift mechanism of heavy-duty vehicle automated manual transmission（AMT）.Taking a shift cylinder for example,the...Based on multidisciplinary design optimization（MDO）,a new design method is put forward for hydraulic shift mechanism of heavy-duty vehicle automated manual transmission（AMT）.Taking a shift cylinder for example,the collaborative optimization（CO）method for the design problem of a cylinder is devided into one system level design optimization problem and three subsystem level design optimization problems.The system level is an economic model and the subsystem level is mechanics,kinetics,and a reliability model.Application of the multidisciplinary design optimization software iSIGHT modeling and solving,optimal solution of the shifting cylinder CO model is obtained.According to the optimal solution,oil cylinders are machined out and installed on the gearbox of an AMT system for the bench cycle shift test.The results show that the output force and action speed of the optimized mechanism can meet requirements very well.In addition,the optimized mechanism has a better performance compared to the structure of the traditional design method,which indicates that the CO method can optimize the design of hydraulic transmission.展开更多
Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional ...Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional collaborative filtering algorithms, this paper is going to come up with improvements and construct a hybrid collaborative filtering algorithm model which will possess excellent scalability. Meanwhile, this paper will also optimize the process based on the parameter selection of genetic algorithm and demonstrate its pseudocode reference so as to provide new ideas and methods for the study of parameter combination optimization in hybrid collaborative filtering algorithm.展开更多
The distribution loads, output of distributed generations (DGs) and dynamic power price present obvious time-sequence property, the typical property is studied in this paper. The model of microgrid (including adjustab...The distribution loads, output of distributed generations (DGs) and dynamic power price present obvious time-sequence property, the typical property is studied in this paper. The model of microgrid (including adjustable load, DGs, storage and dynamic power price) is studied. A multi-timescale collaborative optimization model is built towards microgrid;main measures in different timescale optimization are realized. An improved adaptive genetic algorithm is used to solve the optimization problem, which improved the efficiency and reliability. The proposed optimization model is simulated in IEEE 33 node system;the results show it’s effective.展开更多
Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteri...Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.展开更多
文摘In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1005900the National Natural Science Foundation of China under Grant 62001220+3 种基金the Jiangsu Provincial Key Research and Development Program under Grants BE2022068the Natural Science Foundation of Jiangsu Province under Grants BK20200440the Future Network Scientific Research Fund Project FNSRFP-2021-YB-03the Young Elite Scientist Sponsorship Program,China Association for Science and Technology.
文摘Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.
基金Supported by Jiangsu Provincial Agriculture Science and Technology Innovation Fund(Grant No.CX(23)3036)National Natural Science Foundation of China(Grant No.52375479)+1 种基金Jiangsu Provincal Graduate Research and Practical Innovation Program(Grant No.KYCX24_0825)Changzhou Municipal Sci&Tech Program(Grant No.CM20223014).
文摘When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.
基金This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1005900the National Natural Science Foundation of China under Grant 62001220+3 种基金the Jiangsu Provincial Key Research and Development Program under Grants BE2022068the Natural Science Foundation of Jiangsu Province under Grants BK20200440the Future Network Scientific Research Fund Project FNSRFP-2021-YB-03the Young Elite Scientist Sponsorship Program,China Association for Science and Technology.
文摘Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
基金Knowledge-based Ship-design Hyper-integrated Platform(KSHIP) of Ministry of Education and Ministry of Finance,P. R. China(No.200512)
文摘The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.
基金supported by the National Natural Science Foundation of China (60904002 70971132)
文摘A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to ob- tain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.
文摘Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
基金supported by the“National Natural Science Foundation of China”(Grant Nos.52105106,52305155)the“Jiangsu Province Natural Science Foundation”(Grant Nos.BK20210342,BK20230904)the“Young Elite Scientists Sponsorship Programby CAST”(Grant No.2023JCJQQT061).
文摘To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.
基金supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China(11202318,11203721)the Australian Research Council(DP200100700)。
文摘This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
基金the National Natural Science Foundation of China(Grant Number 52075553)the Postgraduate Research and Innovation Project of Central South University(School-Enterprise Association)(Grant Number 2021XQLH014).
文摘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.
基金supported by National Key R&D Program of China under Grants No.2022YFB4400703National Natural Science Foundation of Heilongjiang Province of China(Outstanding Youth Foundation)under Grants No.JJ2019YX0922 and NSFC under Grants No.F2018006.
基金Supported by National Natural Science Foundation of China(Grant No.51575325)Shandong Provincial Natural Science Foundation of China(Grant No.ZR2013EEM007)
文摘Both the seat and cab system of truck play a vital role in ride comfort.The damping matching methods of the two systems are studied separately at present.However,the driver,seat,and cab system are one inseparable whole.In order to further improve ride comfort,the seat suspension is regarded as the fifth suspension of the cab,a new idea of ＂Five-suspensions＂ is proposed.Based on this idea,a 4 degree-of-freedom driver-seat-cab coupled system model is presented.Using the tested cab suspensions excitations as inputs and seat acceleration response as compared output,the simulation model is built.Taking optimal ride comfort as target,a new method of damping collaborative optimization for Five-suspensions is proposed.With a practical example of seat and cab system,the damping parameters are optimized and validated by simulation and bench test.The results show the seat vertical frequency-weighted RMS acceleration values tested for the un-optimized and optimized Five-suspensions are 0.50 m/s～2 and 0.39 m/s～2,respectively,with a decrease by 22.0%,which proves the model and method proposed are correct and reliable.The idea of ＂Five-suspensions＂ and the method proposed provide a reference for achieving global optimal damping matching of seat suspension and cab suspensions.
基金the National Natural Science Foundation of China(No.71273088).
文摘China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.
基金This paper was supported in part by Natural Science Foundation of Jiangsu Province of China under Grant BK20191381in part by Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 2019K223+2 种基金in part by the National Natural Science Foundation of China under Grant 61802208,Grant 61772286,Grant 61771258,and Grant 61701252in part by Project funded by China Postdoctoral Science Foundation Grant 2019M651923in part by Primary Research&Development Plan of Jiangsu Province under Grant BE2019742,and in part by NUPTSF under Grant NY220060,NY218035.
文摘Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.
基金supported by the National Key Research and Development Program Projects of China(No.2018YFC1504705)the National Natural Science Foundation of China(No.61731015)+1 种基金the Major instrument special project of National Natural Science Foundation of China(No.42027806)the Key Research and Development Program of Shaanxi(No.2022GY-331)。
文摘Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2011AA11A223)
文摘Based on multidisciplinary design optimization（MDO）,a new design method is put forward for hydraulic shift mechanism of heavy-duty vehicle automated manual transmission（AMT）.Taking a shift cylinder for example,the collaborative optimization（CO）method for the design problem of a cylinder is devided into one system level design optimization problem and three subsystem level design optimization problems.The system level is an economic model and the subsystem level is mechanics,kinetics,and a reliability model.Application of the multidisciplinary design optimization software iSIGHT modeling and solving,optimal solution of the shifting cylinder CO model is obtained.According to the optimal solution,oil cylinders are machined out and installed on the gearbox of an AMT system for the bench cycle shift test.The results show that the output force and action speed of the optimized mechanism can meet requirements very well.In addition,the optimized mechanism has a better performance compared to the structure of the traditional design method,which indicates that the CO method can optimize the design of hydraulic transmission.
文摘Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional collaborative filtering algorithms, this paper is going to come up with improvements and construct a hybrid collaborative filtering algorithm model which will possess excellent scalability. Meanwhile, this paper will also optimize the process based on the parameter selection of genetic algorithm and demonstrate its pseudocode reference so as to provide new ideas and methods for the study of parameter combination optimization in hybrid collaborative filtering algorithm.
文摘The distribution loads, output of distributed generations (DGs) and dynamic power price present obvious time-sequence property, the typical property is studied in this paper. The model of microgrid (including adjustable load, DGs, storage and dynamic power price) is studied. A multi-timescale collaborative optimization model is built towards microgrid;main measures in different timescale optimization are realized. An improved adaptive genetic algorithm is used to solve the optimization problem, which improved the efficiency and reliability. The proposed optimization model is simulated in IEEE 33 node system;the results show it’s effective.
基金supported by the National Natural Science Foundation of China(No.61903023)the Natural Science Foundation of Bejing Municipality(No.4204110)+1 种基金State Key Laboratory of Rail Traffic Control and Safety(No.RCS2020ZT006,RCS2021ZT006)the Fundamental Research Funds for the Central Universities(No.2020JBM087).
文摘Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.
