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A collaborative optimization design method of platform location and well trajectory for a complex-structure well factory 认领 引用
1
作者 WANG Ge GAO Deli HUANG Wenjun 《Petroleum Exploration and Development》 SCIE 2026年第1期261-271,共11页
Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drill... Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters. 展开更多
关键词 complex-structure well factory ISSA DABC platform-trajectory collaborative optimization well factory parameter optimization
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Research on Differentiated Characteristics and Collaborative Optimization of Child-Friendly Facility Demands in Parent-Child Tourism in Chongqing 认领 引用
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作者 Qi Huang 《Proceedings of Business and Economic Studies》 2026年第6期82-94,共13页
Driven by demographic structural changes and an upgrade in quality consumption,the parent-child tourism market is transitioning from extensive supply to refined services.Child-friendly facilities are key carriers for ... Driven by demographic structural changes and an upgrade in quality consumption,the parent-child tourism market is transitioning from extensive supply to refined services.Child-friendly facilities are key carriers for enhancing the parent-child tourism experience,with their demands showing significant differentiation across various family structures.Existing research often treats“families”as homogeneous entities,overlooking the demand variations arising from differences in family structures.This study takes Chongqing,a mountain city,as a case study,focusing on four types of families:those with multiple children,intergenerational combinations,single-parent families,and dual-income core families.It employs a mixed-methods approach to reveal the differentiated patterns of demand for child-friendly facilities,identifying four core issues such as supply-demand mismatch and inadequate terrain adaptation.Based on these findings,optimization paths are proposed from four dimensions,including facility layout and service design,providing practical guidance for the high-quality development of parent-child tourism in Chongqing and similar mountainous cities. 展开更多
关键词 Parent-child tourism Child-friendly facilities Demand differentiation Collaborative optimization
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Research on Collaborative Optimization Strategies of New Energy Storage Technologies in Clean Energy Grid Integration 认领 引用
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作者 CHEN Yong ZHANG Jiazhi 《外文科技期刊数据库（文摘版）工程技术》 2026年第1期062-066,共5页
With the growing global demand for clean energy, the grid integration of clean energy faces numerous challenges, and new energy storage technologies have become the key to solving these problems. This paper conducts a... With the growing global demand for clean energy, the grid integration of clean energy faces numerous challenges, and new energy storage technologies have become the key to solving these problems. This paper conducts an in‑depth study on the collaborative optimization strategies of new energy storage technologies in clean energy grid integration, analyzes the characteristics of various new energy storage technologies in detail, discusses their application status and challenges in clean energy grid integration, constructs a collaborative optimization model, and proposes corresponding optimization strategies. The research shows that the rational application of new energy storage technologies and the implementation of collaborative optimization strategies can significantly improve the stability, reliability and economy of clean energy grid integration, providing strong support for promoting the green transformation of energy. 展开更多
关键词 new energy storage technologies clean energy grid integration collaborative optimization stability economy
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Collaborative Quality Management in Industrial Engineering from a Supply Chain Perspective:AI-Driven Enterprise Quality Optimization 认领 引用
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作者 Jiakai Zhong 《Journal of Electronic Research and Application》 2026年第1期43-49,共7页
Amidst the intensifying digital economy and global competition,supply chain quality management is evolving from traditional linear models toward networked systems characterized by data-driven and intelligent collabora... Amidst the intensifying digital economy and global competition,supply chain quality management is evolving from traditional linear models toward networked systems characterized by data-driven and intelligent collaboration.This paper constructs an AI-driven“Supply Chain Quality Collaborative Management”framework through system optimization and artificial intelligence analytical capabilities from a supply chain perspective.The study first analyzes core challenges in supply chain quality collaboration across three dimensions:data fragmentation,standard discrepancies,and mechanism asymmetry.It highlights that traditional static and reactive quality controls struggle to adapt to complex,dynamic supply chain ecosystems.Subsequently,through systematic literature review and theoretical synthesis,the paper elucidates AI’s role in multi-source quality data fusion,semantic alignment,standardized governance,and intelligent incentives.It proposes collaborative optimization pathways based on deep learning,blockchain,and reinforcement learning.Through case studies in the automotive and pharmaceutical industries,the research validates the feasibility of AI in predictive maintenance and cross-linkage collaborative decision-making,demonstrating AI’s ability to significantly enhance the systemic resilience and decision-response capabilities of quality management.This paper innovatively integrates industrial engineering process optimization with cross-organizational governance mechanisms for supply chain quality management,providing a new theoretical framework and practical pathway for intelligent manufacturing and sustainable supply chain development. 展开更多
关键词 Supply chain quality management Industrial engineering Artificial intelligence Quality collaborative optimization Data-driven decision-making
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Fast-zoom and high-resolution sparse compound-eye camera based on dual-end collaborative optimization 认领 引用 被引量：4
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作者 Yi Zheng Hao-Ran Zhang +5 位作者 Xiao-Wei Li You-Ran Zhao Zhao-Song Li Ye-Hao Hou Chao Liu Qiong-Hua Wang 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2025年第6期4-15,共12页
Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution... Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest. 展开更多
关键词 compound-eye camera zoom high resolution collaborative optimization
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Efficient obstacle avoidance planning for multi-robot suspension system based on a collaborative optimization for force and position 认领 引用 被引量：1
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作者 Xiangtang Zhao Zhigang Zhao +2 位作者 Cheng Su Jiadong Meng Hutang Sang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第12期135-154,共20页
To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization met... To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization method for force and position.Based on the analysis of the kinematics and dynamics of the system,the inverse kinematics and inverse dynamics of the system are solved using the least variance method.The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization(SDBO)algorithm,which ensures that the suspended object can move stably to the target point in the workspace.The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable.Finally,the correctness of the obstacle avoidance planning method is verified by simulations.By taking a special scenario,the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51%and the height by 79.88%,and reducing the minimum fitness by 95.84%and the average fitness by 94.77%.The results can help the multi-robot suspension system to perform various towing tasks safely and stably,and extend the related planning and control theory. 展开更多
关键词 Suspension system Obstacle avoidance planning Collision-free feasible space Stable dung beetle optimization Collaborative optimization for force and position
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AI-Driven Interaction and Collaborative Optimization of Vehicle,Charging Station and Grid:Challenges and Prospects 认领 引用
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作者 Bo Yang Zhe Jiang +3 位作者 Jianfeng Wen Ning Yang Kaiping Qu Shuai Zhou 《Energy Engineering》 EI 2025年第6期2187-2195,共9页
1 Introduction Amid escalating global climate change,the“dual carbon”goals of carbon peak and carbon neutrality have become a focal point of global attention and an important strategy for sustainable development[1].... 1 Introduction Amid escalating global climate change,the“dual carbon”goals of carbon peak and carbon neutrality have become a focal point of global attention and an important strategy for sustainable development[1].With the rapid development of renewable energy technologies and the increasing public demand for environmental protection and low-carbon living,the adoption of new energy vehicles,particularly electric vehicles(EVs). 展开更多
关键词 Vehicle-to-grid(V2G)interaction artificial intelligence collaborative optimization electric vehicles grid scheduling
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Collaborative Decomposition Multi-Objective Improved Elephant Clan Optimization Based on Penalty-Based and Normal Boundary Intersection 认领 引用
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作者 Mengjiao Wei Wenyu Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期2505-2523,共19页
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
关键词 Multi-objective optimization elephant clan optimization algorithm collaborative decomposition new individual selection mechanism diversity preservation
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Bi-Level Collaborative Optimization of Electricity-Carbon Integrated Demand Response for Energy-Intensive Industries under Source-Load Interaction 认领 引用
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作者 Huaihu Wang Wen Chen +5 位作者 Jin Yang Rui Su Jiale Li Liao Yuan Zhaobin Du Yujie Meng 《Energy Engineering》 EI 2025年第9期3867-3890,共24页
Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon ... Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems. 展开更多
关键词 Carbon-aware demand response bi-level collaborative optimization dynamic carbon emission factor industrial flexible loads
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Research on the Collaborative Optimization of the Upgrading,Transformation and Maintenance of Large Equipment for Offshore Oil Extraction 认领 引用
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作者 Yanhong Guo 《Journal of Electronic Research and Application》 2025年第6期186-193,共8页
This study investigates the collaborative optimization of upgrading,retrofitting,and maintaining large offshore oil extraction equipment.It examines the key challenges and interdependencies inherent in these processes... This study investigates the collaborative optimization of upgrading,retrofitting,and maintaining large offshore oil extraction equipment.It examines the key challenges and interdependencies inherent in these processes and proposes integrated solutions,including sensor-network-based monitoring and digital twin-driven management platforms.The findings demonstrate notable improvements in operational efficiency and reductions in equipment downtime,underscoring both the economic and safety benefits of the proposed approach and providing a reference framework for future optimization strategies in offshore engineering. 展开更多
关键词 Offshore oil extraction Equipment optimization Collaborative
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Operation Optimization of Microgrid Clusters Coordinated with Distribution Systems with Limited Information Exchange 认领 引用
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作者 Qianfeng Wu Dabo Xie +5 位作者 Wenhua Ni Junjie Zhou Xuantong Lu Chengying Ma Rongqiang Li Yang Li 《Energy Engineering》 EI 2026年第5期229-253,共25页
With the deepening of the power system reform,an increasing number of microgrids are being integrated into the distribution network.In traditional centralized optimization algorithms,the optimal power flow model of th... With the deepening of the power system reform,an increasing number of microgrids are being integrated into the distribution network.In traditional centralized optimization algorithms,the optimal power flow model of the distribution network and the optimal scheduling model of microgrid clusters are directly coupled and solved simultaneously.This process involves extensive information exchange between the upper distribution network system and the lower microgrid clusters,which not only increases the communication burden but also prolongs computation time and raises computational complexity.Moreover,it requires excessive information sharing,making it difficult to achieve limited information exchange between the upper and lower systems.In this paper,an optimization model and solution method based on the analytical target cascading approach are proposed.First,a typical microgrid model is constructed.On this basis,a collaborative optimization model for the active distribution network(ADN)and microgrid clusters is established.The distribution network and the microgrid clusters are treated as a unified entity of interest,with their interconnection power represented as virtual generators and virtual loads to achieve decoupling.Finally,simulations based on the IEEE-33 node standard system are conducted.Compared with the centralized algorithm,the effectiveness of the analytical target cascading method in coordinating the distribution network and microgrid clusters is verified.The proposed approach reduces computational complexity and enables optimized operation with limited information exchange. 展开更多
关键词 Distribution network microgrid clusters analysis target cascading collaborative optimization optimal scheduling limited information exchange
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A Regional Distribution Network Coordinated Optimization Strategy for Electric Vehicle Clusters Based on Parametric Deep Reinforcement Learning 认领 引用
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作者 Lei Su Wanli Feng +4 位作者 Cao Kan Mingjiang Wei Jihai Wang Pan Yu Lingxiao Yang 《Energy Engineering》 EI 2026年第3期195-214,共20页
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s... To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates. 展开更多
关键词 Power system regional distributed energy electric vehicle deep reinforcement learning collaborative optimization
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Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
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作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
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Collaborative optimization of maintenance and spare ordering of continuously degrading systems 认领 引用 被引量：6
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作者 Wei Zhou Dongfeng Wang +1 位作者 Jingyu Sheng Bo Guo 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第1期63-70,共8页
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
关键词 collaborative optimization maintenance spare order- ing degrading system.
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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization 认领 引用 被引量：6
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作者 Jiasen Wang Jun Wang Qing-Long Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第11期1909-1923,共15页
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
关键词 Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles
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Multi-Topology Hierarchical Collaborative Hybrid Particle Swarm Optimization Algorithm for WSN 认领 引用 被引量：3
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作者 Yi Wang Kanqi Wang +2 位作者 Maosheng Zhang Hongzhi Zheng Hui Zhang 《China Communications》 SCIE CSCD 2023年第8期254-275,共22页
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
关键词 particle swarm optimizer levy flight multi-topology hierarchical collaborative framework lamarckian learning intuitive fuzzy entropy wireless sensor network
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Bilateral Collaborative Optimization for Cloud Manufacturing Service 认领 引用 被引量：2
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作者 Bin Xu Yong Tang +3 位作者 Yi Zhu Wenqing Yan Cheng He Jin Qi 《Computers, Materials & Continua》 SCIE EI 2020年第9期2031-2042,共12页
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
关键词 Service composition service scheduling bilateral collaborative optimization evolutionary computation PIO
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Damping Collaborative Optimization of Five-suspensions for Driver-seat-cab Coupled System 认领 引用 被引量：4
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作者 ZHAO Leilei ZHOU Changcheng YU Yuewei 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第4期773-780,共8页
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
关键词 vehicle engineering seat and cab system Five-suspensions damping collaborative optimization
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Improvement of Collaborative Optimization 认领 引用 被引量：3
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作者 黄海燕 王德禹 《Journal of Shanghai Jiaotong university(Science)》 EI 2010年第2期172-177,共6页
This paper presents a new improved collaborative optimization (CO) model that provides solution capabilities for multiobjective multidisciplinary optimization problems. Reasons that cause computational difficulties in... This paper presents a new improved collaborative optimization (CO) model that provides solution capabilities for multiobjective multidisciplinary optimization problems. Reasons that cause computational difficulties in CO algorithm are firstly analyzed. Then a new system level objective function is advised to minimize relative value between the collaborative objective function and single disciplinary objective function. And it eliminates the effect of dimensions and magnitude orders among objectives. A new subsystem level objective function is developed that includes the disciplinary objective function and the consistency constraint. A new CO framework which is more suitable for multilevel distributed design is advised. In this CO framework,the system level optimizer does not only independently invoke the subdisciplinary analysis tools,but also invoke its subdiscipline optimizer. The improved CO model proposed in this work is demonstrated with two examples. The results of examples show the improved CO is not only feasible,reliable and efficient,but also well suitable to solve the multiobjective optimization problems in multidisciplinary design environment. 展开更多
关键词 multidisciplinary design optimization collaborative optimization (CO) multiobjective improvement
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Application of the Improved Collaborative Optimization on Ships’Conceptual Design 认领 引用 被引量：5
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作者 YAO Zhuang-le CHEN Chao-he 《船舶力学》 EI CSCD 北大核心 2014年第12期1453-1459,共7页
The development of multidisciplinary design optimization method and its broad application prospects in ship design are presented.The collaborative optimization is described in detail.According to its disadvantage,a dy... The development of multidisciplinary design optimization method and its broad application prospects in ship design are presented.The collaborative optimization is described in detail.According to its disadvantage,a dynamic penalty function method is proposed.It is based on the modification of the system level optimization problem,and is turned into an unconstrained optimization problems.It can reduce the difficulties and improve the calculation accuracy.A resistance and structural optimization problem of certain SWATH is simplified and solved,and the final result lowers fuel consumption,and shows that this algorithm application in ship's conceptual design is feasible. 展开更多
关键词 collaborative optimization dynamic penalty function small waterplane area twin-hulls ship(SWATH)
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