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Different instances of the problem were solved to optimality in a reasonable computational time, and the results obtained compare favorably with the current practice.
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These two versions were compared with Q-learning, MAXQ, HEXQ and HAMQ-learning for different instances of the distributed hunter prey problem.
The performance of the proposed algorithm has been tested on two different instances of the Moving Peaks Benchmark (MPB) problems.
This paves the way for building bioinformatics applications where the information extracted from previous searches is reused to solve different instances of a similar problem.
The priority-based algorithm was tested against spanning-tree-based algorithm by different instances of small, medium and large-sized problems that outperformed for medium and large-sized problems.
Our system can generate instances of the problem using different distributions of client and mesh routers.
A large variety of problems are special instances of the problem (1.1).
The results were favorable to the metaheuristic algorithm for large instances of the problem.
A tabu search heuristic to find solutions for real-size instances of the problem is proposed.
We then propose a primal decomposition method to solve instances of the problem to optimality.
We also propose two Lagrangean Relaxation-based heuristics to solve larger instances of the problem.
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