Simulated annealing: theory and applications
Simulated annealing: theory and applications
A Fast and Efficient Processor Allocation Scheme for Mesh-Connected Multicomputers
IEEE Transactions on Computers
Simulated Annealing: A Proof of Convergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of allocation algorithms for mesh structured networks with using multistage simulation
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
Simulation-based evaluation of distributed mesh allocation algorithms
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
Evolutionary algorithms for base station placement in mobile networks
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Comparison of allocation algorithms in mesh oriented structures for different scheduling techniques
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
A comparative study between optimization and market-based approaches to multi-robot task allocation
Advances in Artificial Intelligence
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This article contains a short analysis of applying three metaheuristic local search algorithms to solve the problem of allocating two-dimensional tasks on a two-dimensional processor mesh in a period of time. The primary goal is to maximize the level of mesh utilization. To achieve this task we adapted three algorithms: Tabu Search, Simulated Annealing and Random Search, as well as created a helper algorithm Dumb Fit and adapted another helper algorithm - First Fit. To measure the algorithms' efficiency we introduced our own evaluating function Cumulative Effectiveness and a derivative Utilization Factor. Finally, we implemented an experimentation system to test these algorithms on different sets of tasks to allocate. In this article there is a short analysis of series of experiments conducted on three different classes of task sets: small tasks, mixed tasks and large tasks.