期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到60篇文章
< 1 2 3 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
A Survey of Hybrid Energy-Aware and Decentralized Game-Theoretic Approaches in Intelligent Multi-Robot Task Allocation 认领 引用
1
作者 Ali Hamidoğlu Ali Elghirani +1 位作者 Ömer Melih Gül Seifedine Kadry 《Computers, Materials & Continua》 SCIE EI 2026年第6期143-183,共41页
Multi-Robot Task Allocation(MRTA)has proven its importance in the current and near-future era,wherein in every aspect of life,there will be robots to handle tasks effectively and efficiently.While there has been a gro... Multi-Robot Task Allocation(MRTA)has proven its importance in the current and near-future era,wherein in every aspect of life,there will be robots to handle tasks effectively and efficiently.While there has been a growing interest in MRTA problems in the robotics industry,the question arises of how to make robots more decentralized and intelligent through rational decision-makers rather than ones that are centralized and filled with black boxes.This survey aims to address that question by examining recent MRTA literature and exploring topics including MRTA taxonomy,centralized and decentralized controls,static and dynamic allocation strategies,heterogeneity and coalition formations,energy harvesting,game theory,and hybrid MRTA strategies for intelligent task management and decision-making.The main motivation of this survey is to establish a unique understanding of intelligent MRTA by comparing the existing literature through diverse MRTA perspectives.In this regard,we perform a comprehensive analysis of the recent MRTA papers and provide relative discussions in each section.With this survey,we try to pose several open problems and research directions regarding how MRTA evolves when there is a limited energy source or harvesting,how decentralized MRTA mechanisms are involved in robot intelligent decision-making,and how hybrid,learning-based game-theoretical models ease MRTA problems in real-time and on a large scale.Furthermore,this survey also aims to examine the possibility of integrating game theory-based MRTA methodologies including Nash,Stackelberg,and coalition games with hybrid multi-objective optimization methods.Based on the steps taken,this study collects various pieces of information from the literature to form a cohesive MRTA survey.Hence,the shortcomings of existing methods can be identified so that potential research directions can be outlined,particularly research related to energy-efficient,decentralized autonomous MRTA using game theory concepts. 展开更多
关键词 Multi-robot task allocation game theory energy harvesting decentralized networks
暂未订购 下载PDF
Air-to-ground reconnaissance-attack task allocation for heterogeneous UAV swarm 认领 引用 被引量：3
2
作者 LUO Yuelong JIANG Xiuqiang +1 位作者 ZHONG Suchuan JI Yuandong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第1期155-175,共21页
A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV s... A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV swarm needs to detect the environment first and then attack the detected targets.The heterogeneity of UAVs,multiple types of tasks,and the dynamic nature of task environment lead to uneven load and time sequence problems.This paper proposes an improved contract net protocol (CNP) based task allocation scheme,which effectively balances the load of UAVs and improves the task efficiency.Firstly,two types of task models are established,including regional reconnaissance tasks and target attack tasks.Secondly,for regional reconnaissance tasks,an improved CNP algorithm using the uncertain contract is developed.Through uncertain contracts,the area size of the regional reconnaissance task is determined adaptively after this task assignment,which can improve reconnaissance efficiency and resource utilization.Thirdly,for target attack tasks,an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation.Finally,the effectiveness and advantages of the improved method are verified through comparison simulations. 展开更多
关键词 unmanned aerial vehicle(UAV)swarm reconnaissance-attack coupled task allocation contract net protocol(CNP) fuzzy integrated evaluation double-layer negotiation
暂未订购 下载PDF
Distributed adaptive coalition task allocation based on reachable region online prediction for munition swarm 认领 引用 被引量：1
3
作者 Yuze Zhou Teng Long +2 位作者 Jingliang Sun Junzhi Li Jianxin Zhong 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第11期169-183,共15页
Task allocation for munition swarms is constrained by reachable region limitations and real-time requirements.This paper proposes a reachable region guided distributed coalition formation game(RRGDCF)method to address... Task allocation for munition swarms is constrained by reachable region limitations and real-time requirements.This paper proposes a reachable region guided distributed coalition formation game(RRGDCF)method to address these issues.To enable efficient online task allocation,a reachable region prediction strategy based on fully connected neural networks(FCNNs)is developed.This strategy integrates high-fidelity data generated from the golden section method and low-fidelity data from geometric approximation in an optimal mixing ratio to form multi-fidelity samples,significantly enhancing prediction accuracy and efficiency under limited high-fidelity samples.These predictions are then incorporated into the coalition formation game framework.A tabu search mechanism guided by the reachable region center directs munitions to execute tasks within their respective reachable regions,mitigating redundant operations on ineffective coalition structures.Furthermore,an adaptive guidance coalition formation strategy optimizes allocation plans by leveraging the hit probabilities of munitions,replacing traditional random coalition formation methods.Simulation results demonstrate that RRGDCF surpasses the contract network protocol and traditional coalition formation game algorithms in optimality and computational efficiency.Hardware experiments further validate the method's practicality in dynamic scenarios. 展开更多
关键词 Munition swarm Reachable region Coalition formation game Distributed task allocation
暂未订购 下载PDF
Efficient Task Allocation for Energy and Execution Time Trade-Off in Edge Computing Using Multi-Objective IPSO 认领 引用 被引量：1
4
作者 Jafar Aminu Rohaya Latip +2 位作者 Zurina Mohd Hanafi Shafinah Kamarudin Danlami Gabi 《Computers, Materials & Continua》 SCIE EI 2025年第8期2989-3011,共23页
As mobile edge computing continues to develop,the demand for resource-intensive applications is steadily increasing,placing a significant strain on edge nodes.These nodes are normally subject to various constraints,fo... As mobile edge computing continues to develop,the demand for resource-intensive applications is steadily increasing,placing a significant strain on edge nodes.These nodes are normally subject to various constraints,for instance,limited processing capability,a few energy sources,and erratic availability being some of the common ones.Correspondingly,these problems require an effective task allocation algorithmto optimize the resources through continued high system performance and dependability in dynamic environments.This paper proposes an improved Particle Swarm Optimization technique,known as IPSO,for multi-objective optimization in edge computing to overcome these issues.To this end,the IPSO algorithm tries to make a trade-off between two important objectives,which are energy consumption minimization and task execution time reduction.Because of global optimal position mutation and dynamic adjustment to inertia weight,the proposed optimization algorithm can effectively distribute tasks among edge nodes.As a result,it reduces the execution time of tasks and energy consumption.In comparative assessments carried out by IPSO with benchmark methods such as Energy-aware Double-fitness Particle Swarm Optimization(EADPSO)and ICBA,IPSO provides better results than these algorithms.For the maximum task size,when compared with the benchmark methods,IPSO reduces the execution time by 17.1%and energy consumption by 31.58%.These results allow the conclusion that IPSO is an efficient and scalable technique for task allocation at the edge environment.It provides peak efficiency while handling scarce resources and variable workloads. 展开更多
关键词 Keyword edge computing energy consumption execution time particle swarm optimization task allocation
暂未订购 下载PDF
Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding 认领 引用 被引量：69
5
作者 Zhu WANG Li LIU +1 位作者 Teng LONG Yonglu WENa 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2018年第2期339-350,共12页
This paper presents a novel multiple Unmanned Aerial Vehicles（UAVs） reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs＇ task sequence. Heterogeneous t... This paper presents a novel multiple Unmanned Aerial Vehicles（UAVs） reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs＇ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor＇s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs＇ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem（MDTSP）, where visit paths to the heterogeneous targets must meet specific constraints due to the targets＇ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators（OGA-DEMMO） is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGADEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems. 展开更多
关键词 Unmanned aerial vehicles Task allocation Genetic algorithm Travelling salesman problems Dubins vehicles
暂未订购 下载PDF
Distributed dynamic task allocation for unmanned aerial vehicle swarm systems:A networked evolutionary game-theoretic approach 认领 引用 被引量：14
6
作者 Zhe ZHANG Ju JIANG +1 位作者 Haiyan XU Wen-An ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第6期182-204,共23页
Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performan... Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm. 展开更多
关键词 Task allocation Unmanned Aerial Vehicles(UAV) Game theory Log-linear learning Distributed optimization algorithm
暂未订购 下载PDF
Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources 认领 引用 被引量：8
7
作者 Danfeng Wu Guangping Zeng +2 位作者 Lingguo Meng Weijian Zhou Linmin Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第1期155-168,共14页
Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy sup... Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals. 展开更多
关键词 Energy resource constraints Gini coefficient multi-robot systems task allocation
暂未订购 下载PDF
A blockchain bee colony double inhibition labor division algorithm for spatio-temporal coupling task with application to UAV swarm task allocation 认领 引用 被引量：10
8
作者 WU Husheng LI Hao XIAO Renbin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第5期1180-1199,共20页
It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clu... It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance. 展开更多
关键词 bee colony double inhibition labor division algorithm high dimensional attribute sudden task reforming the task chain task allocation model
暂未订购 下载PDF
Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing 认领 引用 被引量：3
9
作者 Jiabao Wen Jiachen Yang +2 位作者 Tianying Wang Yang Li Zhihan Lv 《Digital Communications and Networks》 SCIE CSCD 2023年第2期473-482,共10页
To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel c... To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing. 展开更多
关键词 Wireless sensor network Parallel computation Task allocation Genetic algorithm Ant colony optimization algorithm Energy-efficient Load balancing
暂未订购 下载PDF
Multi-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning 认领 引用 被引量：5
10
作者 苗镇华 黄文焘 +1 位作者 张依恋 范勤勤 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第3期377-387,共11页
The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi... The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems. 展开更多
关键词 multi-robot task allocation multi-robot cooperation path planning multimodal multi-objective evo-lutionary algorithm deep reinforcement learning
暂未订购 下载PDF
Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm 认领 引用 被引量：41
11
作者 WANG Jian-feng JIA Gao-wei +1 位作者 LIN Jun-can HOU Zhong-xi 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第2期432-448,共17页
The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo... The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments. 展开更多
关键词 unmanned aerial vehicles cooperative task allocation heterogeneous constraint multi-objective optimization solution evaluation method
暂未订购 下载PDF
Genetic Algorithm Based Combinatorial Auction Method for Multi-Robot Task Allocation 认领 引用 被引量：1
12
作者 龚建伟 黄宛宁 +1 位作者 熊光明 满益明 《Journal of Beijing Institute of Technology》 EI CAS 2007年第2期151-156,共6页
An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auctio... An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation. 展开更多
关键词 multi-robot task allocation combinatorial auctions genetic algorithm
暂未订购 下载PDF
Multi-robot task allocation for exploration 认领 引用 被引量：5
13
作者 高平安 蔡自兴 《Journal of Central South University of Technology》 2006年第5期548-551,共4页
The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional... The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid. 展开更多
关键词 multi-robot systems task allocation average path cost multi-round single-item auction target tree
暂未订购 下载PDF
MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge 认领 引用 被引量：1
14
作者 Tengda Li Gang Wang Qiang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2559-2586,共28页
Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinfor... Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA. 展开更多
关键词 Deep reinforcement learning dynamic task allocation intelligent decision-making multi-agent system MADDPG-D2 algorithm
暂未订购 下载PDF
TASK ALLOCATION BASED ON PHEROMONE 认领 引用 被引量：1
15
作者 王雷 唐敦兵 凌雪 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第4期331-337,共7页
A pheromone-based coordination approach and a prototype implementation for task allocation are pro- posed, for dealing with the static task allocation. The approach uses an indirect coordination mechanism, called pher... A pheromone-based coordination approach and a prototype implementation for task allocation are pro- posed, for dealing with the static task allocation. The approach uses an indirect coordination mechanism, called pheromone, which comes from the collective behavior of ant colonies for food foraging. Therefore it can automat- ically find efficient manufacturing cells for processing tasks and reduce communication overhead, which exists in the contract net protocol. Experimental results confirm that the approach has excellent stability and optimization ability for task allocation problems in a static environment. 展开更多
关键词 pheromone task allocation coordination model implicit coordination
暂未订购 下载PDF
UAV Task Allocation for Hierarchical Multiobjective Optimization in Complex Conditions Using Modified NSGA-III with Segmented Encoding 认领 引用 被引量：1
16
作者 JIN Yudong FENG Jiabo ZHANG Weijun 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第4期431-445,共15页
With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring.... With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring. Task allocation for UAVs is the process of planning the division of work among UAVs, controlled from ground stations by human operators. This study formulates the UAV task-allocation problem as an extended traveling salesman problem and presents a novel UAV task-allocation model for complex air concentration monitoring tasks. Then, an optimized non-dominated sorting genetic algorithm III (NSGA-III) based on a twin-exclusion mechanism, hierarchical objective-domination operator, and segmented gene encoding (i.e., NSGA-III-TEHOD) is developed to solve complex task-allocation problems involving multiple UAVs, hierarchical objectives, obstacles, and ambient wind. The algorithm is tested in several simulations, and the results demonstrate that the new algorithm outperforms NSGA-III, non-dominated sorting genetic algorithm II (NSGA-II), and genetic algorithm (GA) in terms of efficiency of global convergence and early maturation prevention and is available for the hierarchical objective-optimization problems. 展开更多
关键词 unmanned aerial vehicle(UAV) task allocation non-dominated sorting genetic algorithm(NSGA) multiobjective optimization
暂未订购 下载PDF
Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles 认领 引用
17
作者 Neng Wan Guangping Zeng Xianwei Zhou 《Computers, Materials & Continua》 SCIE EI 2024年第9期4281-4302,共22页
With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)... With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%. 展开更多
关键词 Task allocation intelligent connected vehicles dispersed computing matching algorithm
暂未订购 下载PDF
Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing 认领 引用 被引量：4
18
作者 Xu Wenjing Wang Wei +2 位作者 Li Zuguang Wu Qihui Wang Xianbin 《China Communications》 SCIE CSCD 2024年第4期218-229,共12页
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t... Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case. 展开更多
关键词 blockchain collaborative edge computing resource optimization task allocation
暂未订购 下载PDF
A METHOD OF TASK ALLOCATION AND AUTOMATED NEGOTIATION FOR MULTI ROBOTS 认领 引用 被引量：2
19
作者 Ke Wende Peng Zhiping +3 位作者 Yuan Quande Hong Bingrong Chen Ke Cai Zesu 《Journal of Electronics(China)》 2012年第6期541-549,共9页
A method of task allocation and automated negotiation for multi robots was proposed. Firstly, the principles of task allocation were described based on the real capability of robot. Secondly, the model of automated ne... A method of task allocation and automated negotiation for multi robots was proposed. Firstly, the principles of task allocation were described based on the real capability of robot. Secondly, the model of automated negotiation was constructed, in which Least-Squares Support Vector Regression (LSSVR) was improved to estimate the opponent's negotiation utility and the robust controller of output feedback was employed to optimize the utility performance indicators. Thirdly, the protocol of negotiation and reallocation was proposed to improve the real-time capability and task allocation. Finally, the validity of method was proved through experiments. 展开更多
关键词 Distributed Robot Task allocation Negotiation Communication
暂未订购 下载PDF
Hybrid Two-Phase Task Allocation for Mobile Crowd Sensing 认领 引用 被引量：1
20
作者 LIU Jiahao JIN Hanxin +3 位作者 QIANG Lei GAO Guoju DU Yang HUANG He 《计算机工程》 CAS CSCD 北大核心 2022年第3期139-145,共7页
As a result of the popularity of mobile devices,Mobile Crowd Sensing (MCS) has attracted a lot of attention. Task allocation is a significant problem in MCS. Most previous studies mainly focused on stationary spatial ... As a result of the popularity of mobile devices,Mobile Crowd Sensing (MCS) has attracted a lot of attention. Task allocation is a significant problem in MCS. Most previous studies mainly focused on stationary spatial tasks while neglecting the changes of tasks and workers. In this paper,the proposed hybrid two-phase task allocation algorithm considers heterogeneous tasks and diverse workers.For heterogeneous tasks,there are different start times and deadlines. In each round,the tasks are divided into urgent and non-urgent tasks. The diverse workers are classified into opportunistic and participatory workers.The former complete tasks on their way,so they only receive a fixed payment as employment compensation,while the latter commute a certain distance that a distance fee is paid to complete the tasks in each round as needed apart from basic employment compensation. The task allocation stage is divided into multiple rounds consisting of the opportunistic worker phase and the participatory worker phase. At the start of each round,the hiring of opportunistic workers is considered because they cost less to complete each task. The Poisson distribution is used to predict the location that the workers are going to visit,and greedily choose the ones with high utility. For participatory workers,the urgent tasks are clustered by employing hierarchical clustering after selecting the tasks from the uncompleted task set.After completing the above steps,the tasks are assigned to participatory workers by extending the Kuhn-Munkres (KM) algorithm.The rest of the uncompleted tasks are non-urgent tasks which are added to the task set for the next round.Experiments are conducted based on a real dataset,Brightkite,and three typical baseline methods are selected for comparison. Experimental results show that the proposed algorithm has better performance in terms of total cost as well as efficiency under the constraint that all tasks are completed. 展开更多
关键词 Mobile Crowd Sensing(MCS) two-phase task allocation Kuhn-Munkres(KM)algorithm opportunistic worker participatory worker
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 3 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈