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Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study...
Multi-robot coalition formation (MRCF) problem deals with the formation of subsets of robotic to handle a particular task. In such a system, every task is executed by multiple robots. Thus, cooperation and coordination among the robots is very important. One of the key issues to be investigated for smooth operation of a multi-robot systems is finding an optimal task allocation among the suitably formed...
Real time strategy games are complex scenarioswhere multiple agents must be coordinated in a dynamic,partially observable environment. In this work, we model thecoordination of these agents as a task allocation problem, in which specific tasks are given to the agents that are more suited to execute them. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using...
Task allocation and scheduling in MAS systems utilized genetic algorithm is a focus for more and more computer scholars. Aiming at the low speed of typical genetic algorithm, the global convergence for traditional genetic algorithm, and the local convergence for simulated annealing algorithm, this paper proposes a new task allocation algorithm in multiple Agent systems with the advantages of both...
Uninhabited combat aerial vehicles (UCAVs) task allocation problem is one of the most important issues in UCAV research. This paper refers to an extension of the mixed-integer linear programming (MILP) task allocation model. Through analyzing the model characteristics, and combining with the advantages and disadvantages of co-evolutionary genetic algorithm, this paper modifies the mechanism of search...
Several researchers have explored task allocation in depth and created decision models which focus on allocating work to different sites. In this paper we focus on allocation of individual tasks to resources across locations and propose a semi-automated, assisted model for task allocation using genetic algorithms. This mechanism has been created and tested for a project scenario.
Task allocation and knowledge workers scheduling is known as an NP-hard problem. Scientific task allocation and knowledge workers scheduling is an important part of rational human resources management in enterprises. Particle swarm optimization (PSO) has few parameters to adjust and is easy to implement. This paper uses PSO to research task allocation and knowledge workers scheduling. Particle swarm...
Task scheduling and task allocation, which are vital parts of mapping parallel programs to concurrent architectures, must take into account the interprocessor communication, whose overheads have emerged as the major performance limitation in parallel applications. Furthermore, its power consumption is an important research focus which must be addressed. Finding an optimal solution requires information...
The work is devoted to solve allocation task problem in the distributed way in multi agents systems with multi-objective genetic algorithms. The paper shows the main advantages of genetic algorithms and the way to apply a new genetic operator using the solution information of the other agents for save time in the search a expand the solution of the optimal space.
It is a primary research problem in multi-robot cooperation domain to allocate task among robots so as to obtain maximal utility. On the base of presenting the utility values matrix for n robots relative to n tasks, and from the view of some disadvantages on computation complexity and bad real-time of Hungarian Algorithm, we present a new approach using genetic algorithm to seek for the optimal scheme...
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