This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.展开更多
Solving the path planning problem of Autonomous Underwater Vehicles(AUVs)is crucial for reducing energy waste and improving operational efficiency.However,two main challenges hinder further development:Firstly,existin...Solving the path planning problem of Autonomous Underwater Vehicles(AUVs)is crucial for reducing energy waste and improving operational efficiency.However,two main challenges hinder further development:Firstly,existing algorithms often treat this as a single-objective optimization problem,whereas in reality,it should be multi-objective,considering factors such as distance,safety,and smoothness simultaneously.Secondly,the limited availability of optimization results arises due to they are single-path,which fail to meet real-world conditions.To address these challenges,first of all,an improved AUV path planning model is proposed,in which the collisions of path and obstacles are classified more specifically.Subsequently,a novel Altruistic Nurturing Algorithm(ANA)inspired by natural altruism is introduced.In the algorithm,nurturing cost considering Pareto rank and crowd distance is introduced as guidance of evolution to avoid futile calculation,abandonment threshold is self-adaptive with descendant situation to help individuals escape from local optima and double selection strategy combining crowd and k-nearest neighbors selection helps to get a better-distributed Pareto front.Experimental results comparing ANA with existing algorithms in AUV path planning demonstrate its superiority.Finally,a user-friendly interface,the Multi-Objective AUV Path Planner,is designed to provide users with a group of paths for informed decisionmaking.展开更多
Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution reliability.During the practical execution phase,there are inevitable risks where UAVs being destroyed o...Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution reliability.During the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be executed.To improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this paper.In the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model.And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution.As in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario.And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention.Three cases of different scales demonstrate that the two modules cooperate well with each other.On the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced.On the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond.The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end manufacturing.Current imaging systems often exhibit inflexibility,being confined to specific objects and encounter...Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end manufacturing.Current imaging systems often exhibit inflexibility,being confined to specific objects and encountering difficulties with diverse industrial structures lacking standardized computer-aided design(CAD)models or in instances of deformation.Inspired by the multidimensional observation of humans,our study introduces a universal image acquisition paradigm tailored for robotics,seamlessly integrating multi-objective optimization trajectory planning and control scheme to harness measured point clouds for versatile,efficient,and highly accurate image acquisition across diverse structures and scenarios.Specifically,we introduce an energybased adaptive trajectory optimization(EBATO)method that combines deformation and deviation with dual-threshold optimization and adaptive weight adjustment to improve the smoothness and accuracy of imaging trajectory and posture.Additionally,a multi-optimization control scheme based on a meta-heuristic beetle antennal olfactory recurrent neural network(BAORNN)is proposed to track the imaging trajectory while addressing posture,obstacle avoidance,and physical constraints in industrial scenarios.Simulations,real-world experiments,and comparisons demonstrate the effectiveness and practicality of the proposed paradigm.展开更多
The close proximity and the necessity of coordination between multiple high-voltage direct currents(HVDCs)raise the issue of grid partitioning in multi-infeed HVDC systems.A multi-objective partition strategy is propo...The close proximity and the necessity of coordination between multiple high-voltage direct currents(HVDCs)raise the issue of grid partitioning in multi-infeed HVDC systems.A multi-objective partition strategy is proposed in this paper.Several types of relationships to be coordinated and complemented are analyzed and formulated using quantitative indices.According to the graph theory,the HVDC partition is transformed into a graph-cut problem and solved via the spectral clustering algorithm.Finally,the proposed method is validated for a practical multi-HVDC grid,confirming its feasibility and effectiveness.展开更多
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-d...The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.展开更多
The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving...The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving timely and compatible solutions to treat diverse skin injuries.In situ bioprinting has emerged as a key new technology,since it reduces risks during the implantation of printed scaffolds and demonstrates superior therapeutic effects.However,maintaining printing fidelity during in situ bioprinting remains a critical challenge,particularly with respect to model layering and path planning.This study proposes a novel optimization-based conformal path planning strategy for in situ bioprinting-based repair of complex skin injuries.This strategy employs constrained optimization to identify optimal waypoints on a point cloud-approximated curved surface,thereby ensuring a high degree of similarity between predesigned planar and surface-mapped 3D paths.Furthermore,this method is applicable for skin wound treatments,since it generates 3D-equidistant zigzag curves along surface tangents and enables multi-layer conformal path planning to facilitate the treatment of volumetric injuries.Furthermore,the proposed algorithm was found to be a feasible and effective treatment in a murine back injury model as well as in other complex models,thereby showcasing its potential to guide in situ bioprinting,enhance bioprinting fidelity,and facilitate improvement of clinical outcomes.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-int...As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.展开更多
Trochoidal milling is known for its advantages in machining difficult-to-machine materials as it facilitates chip removal and tool cooling.However,the conventional trochoidal tool path presents challenges such as lowe...Trochoidal milling is known for its advantages in machining difficult-to-machine materials as it facilitates chip removal and tool cooling.However,the conventional trochoidal tool path presents challenges such as lower machining efficiency and longer machining time due to its time-varying cutter-workpiece engagement angle and a high percentage of non-cutting tool paths.To address these issues,this paper introduces a parameter-variant trochoidal-like(PVTR)tool path planning method for chatter-free and high-efficiency milling.This method ensures a constant engagement angle for each tool path period by adjusting the trochoidal radius and step.Initially,the nonlinear equation for the PVTR toolpath is established.Then,a segmented recurrence method is proposed to plan tool paths based on the desired engagement angle.The impact of trochoidal tool path parameters on the engagement angle is analyzed and coupled this information with the milling stability model based on spindle speed and engagement angle to determine the desired engagement angle throughout the machining process.Finally,several experimental tests are carried out using the bull-nose end mill to validate the feasibility and effectiveness of the proposed method.展开更多
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distrib...To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed.Firstly,the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model,and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios.Finally,considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system peaking,we propose the optimization model of high-voltage distribution network energy storage plant siting and capacity setting for source-storage cooperative peaking.The simulation results show that the proposed energy storage plant planning method can effectively alleviate the branch circuit blockage,promote new energy consumption,reduce the burden of the main grid peak shifting,and leave sufficient peak shifting margin for the subsequent development of a new energy distribution network while ensuring the economy.展开更多
Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network coverage.In natural disasters,timely delivery of first aid supplies is crucial.Current UAVs face risks such as...Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network coverage.In natural disasters,timely delivery of first aid supplies is crucial.Current UAVs face risks such as crashing into birds or unexpected structures.Airdrop systems with parachutes risk dispersing payloads away from target locations.The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations.The civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and capability.Deep Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces.Earlier strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster environments.This paper introduces a new DQN framework to tackle challenges in disaster environments.It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and Q-networks.Additionally,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings.The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.展开更多
Small modular reactor(SMR)belongs to the research forefront of nuclear reactor technology.Nowadays,advancement of intelligent control technologies paves a new way to the design and build of unmanned SMR.The autonomous...Small modular reactor(SMR)belongs to the research forefront of nuclear reactor technology.Nowadays,advancement of intelligent control technologies paves a new way to the design and build of unmanned SMR.The autonomous control process of SMR can be divided into three stages,say,state diagnosis,autonomous decision-making and coordinated control.In this paper,the autonomous state recognition and task planning of unmanned SMR are investigated.An operating condition recognition method based on the knowledge base of SMR operation is proposed by using the artificial neural network(ANN)technology,which constructs a basis for the state judgment of intelligent reactor control path planning.An improved reinforcement learning path planning algorithm is utilized to implement the path transfer decision-makingThis algorithm performs condition transitions with minimal cost under specified modes.In summary,the full range control path intelligent decision-planning technology of SMR is realized,thus provides some theoretical basis for the design and build of unmanned SMR in the future.展开更多
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
基金supported by the Guangzhou City School Joint Found Project(SL2022A03J01009)the National Nature Science Foundation of China(Grant No.51975135)the Natural Science Foundation of Guangdong Province(2018A030310063).
文摘Solving the path planning problem of Autonomous Underwater Vehicles(AUVs)is crucial for reducing energy waste and improving operational efficiency.However,two main challenges hinder further development:Firstly,existing algorithms often treat this as a single-objective optimization problem,whereas in reality,it should be multi-objective,considering factors such as distance,safety,and smoothness simultaneously.Secondly,the limited availability of optimization results arises due to they are single-path,which fail to meet real-world conditions.To address these challenges,first of all,an improved AUV path planning model is proposed,in which the collisions of path and obstacles are classified more specifically.Subsequently,a novel Altruistic Nurturing Algorithm(ANA)inspired by natural altruism is introduced.In the algorithm,nurturing cost considering Pareto rank and crowd distance is introduced as guidance of evolution to avoid futile calculation,abandonment threshold is self-adaptive with descendant situation to help individuals escape from local optima and double selection strategy combining crowd and k-nearest neighbors selection helps to get a better-distributed Pareto front.Experimental results comparing ANA with existing algorithms in AUV path planning demonstrate its superiority.Finally,a user-friendly interface,the Multi-Objective AUV Path Planner,is designed to provide users with a group of paths for informed decisionmaking.
基金supported by the National Key Research and Development Plan(Grant No.2021YFB3302501)the National Natural Science Foundation of China(Grant Nos.12102077,12161076,U2241263).
文摘Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution reliability.During the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be executed.To improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this paper.In the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model.And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution.As in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario.And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention.Three cases of different scales demonstrate that the two modules cooperate well with each other.On the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced.On the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond.The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金supported in part by the National Natural Science Foundation of China(62303457,U21A20482)China Postdoctoral Science Foundation(2023M733737)the National Key Research and Development Program of China(2022YFB3303800)。
文摘Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end manufacturing.Current imaging systems often exhibit inflexibility,being confined to specific objects and encountering difficulties with diverse industrial structures lacking standardized computer-aided design(CAD)models or in instances of deformation.Inspired by the multidimensional observation of humans,our study introduces a universal image acquisition paradigm tailored for robotics,seamlessly integrating multi-objective optimization trajectory planning and control scheme to harness measured point clouds for versatile,efficient,and highly accurate image acquisition across diverse structures and scenarios.Specifically,we introduce an energybased adaptive trajectory optimization(EBATO)method that combines deformation and deviation with dual-threshold optimization and adaptive weight adjustment to improve the smoothness and accuracy of imaging trajectory and posture.Additionally,a multi-optimization control scheme based on a meta-heuristic beetle antennal olfactory recurrent neural network(BAORNN)is proposed to track the imaging trajectory while addressing posture,obstacle avoidance,and physical constraints in industrial scenarios.Simulations,real-world experiments,and comparisons demonstrate the effectiveness and practicality of the proposed paradigm.
基金supported by the Science and Technology Project of State Grid Corporation of China:“Control Strategy Optimization Technology for Large-Scale Photovoltaic Power Generation on the Sending-end and Receiving-end of DC Power System”(4000-201934198A-0-0-00)
文摘The close proximity and the necessity of coordination between multiple high-voltage direct currents(HVDCs)raise the issue of grid partitioning in multi-infeed HVDC systems.A multi-objective partition strategy is proposed in this paper.Several types of relationships to be coordinated and complemented are analyzed and formulated using quantitative indices.According to the graph theory,the HVDC partition is transformed into a graph-cut problem and solved via the spectral clustering algorithm.Finally,the proposed method is validated for a practical multi-HVDC grid,confirming its feasibility and effectiveness.
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金supported by the National Key R&D Program of China(No.2022YFB3104502)the National Natural Science Foundation of China(No.62301251)+2 种基金the Natural Science Foundation of Jiangsu Province of China under Project(No.BK20220883)the open research fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2024D04)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
基金supported in part by the National Natural Science Foundation of China(Nos.52205532 and 624B2077)the National Key Research and Development Program of China(No.2023YFB4302003).
文摘The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving timely and compatible solutions to treat diverse skin injuries.In situ bioprinting has emerged as a key new technology,since it reduces risks during the implantation of printed scaffolds and demonstrates superior therapeutic effects.However,maintaining printing fidelity during in situ bioprinting remains a critical challenge,particularly with respect to model layering and path planning.This study proposes a novel optimization-based conformal path planning strategy for in situ bioprinting-based repair of complex skin injuries.This strategy employs constrained optimization to identify optimal waypoints on a point cloud-approximated curved surface,thereby ensuring a high degree of similarity between predesigned planar and surface-mapped 3D paths.Furthermore,this method is applicable for skin wound treatments,since it generates 3D-equidistant zigzag curves along surface tangents and enables multi-layer conformal path planning to facilitate the treatment of volumetric injuries.Furthermore,the proposed algorithm was found to be a feasible and effective treatment in a murine back injury model as well as in other complex models,thereby showcasing its potential to guide in situ bioprinting,enhance bioprinting fidelity,and facilitate improvement of clinical outcomes.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
基金funded by the Technology Project of State Grid Jibei Electric Power Supply Co.,Ltd.(Grant Number:52018F240001).
文摘As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20202 and 52275477).
文摘Trochoidal milling is known for its advantages in machining difficult-to-machine materials as it facilitates chip removal and tool cooling.However,the conventional trochoidal tool path presents challenges such as lower machining efficiency and longer machining time due to its time-varying cutter-workpiece engagement angle and a high percentage of non-cutting tool paths.To address these issues,this paper introduces a parameter-variant trochoidal-like(PVTR)tool path planning method for chatter-free and high-efficiency milling.This method ensures a constant engagement angle for each tool path period by adjusting the trochoidal radius and step.Initially,the nonlinear equation for the PVTR toolpath is established.Then,a segmented recurrence method is proposed to plan tool paths based on the desired engagement angle.The impact of trochoidal tool path parameters on the engagement angle is analyzed and coupled this information with the milling stability model based on spindle speed and engagement angle to determine the desired engagement angle throughout the machining process.Finally,several experimental tests are carried out using the bull-nose end mill to validate the feasibility and effectiveness of the proposed method.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金supported by State Grid Anhui Electric Power Co.,Ltd.Research Program(B3120923000C).
文摘To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed.Firstly,the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model,and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios.Finally,considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system peaking,we propose the optimization model of high-voltage distribution network energy storage plant siting and capacity setting for source-storage cooperative peaking.The simulation results show that the proposed energy storage plant planning method can effectively alleviate the branch circuit blockage,promote new energy consumption,reduce the burden of the main grid peak shifting,and leave sufficient peak shifting margin for the subsequent development of a new energy distribution network while ensuring the economy.
基金supported by the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan under Grant No.249015/0224.
文摘Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network coverage.In natural disasters,timely delivery of first aid supplies is crucial.Current UAVs face risks such as crashing into birds or unexpected structures.Airdrop systems with parachutes risk dispersing payloads away from target locations.The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations.The civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and capability.Deep Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces.Earlier strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster environments.This paper introduces a new DQN framework to tackle challenges in disaster environments.It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and Q-networks.Additionally,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings.The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.
文摘Small modular reactor(SMR)belongs to the research forefront of nuclear reactor technology.Nowadays,advancement of intelligent control technologies paves a new way to the design and build of unmanned SMR.The autonomous control process of SMR can be divided into three stages,say,state diagnosis,autonomous decision-making and coordinated control.In this paper,the autonomous state recognition and task planning of unmanned SMR are investigated.An operating condition recognition method based on the knowledge base of SMR operation is proposed by using the artificial neural network(ANN)technology,which constructs a basis for the state judgment of intelligent reactor control path planning.An improved reinforcement learning path planning algorithm is utilized to implement the path transfer decision-makingThis algorithm performs condition transitions with minimal cost under specified modes.In summary,the full range control path intelligent decision-planning technology of SMR is realized,thus provides some theoretical basis for the design and build of unmanned SMR in the future.
