Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Parallel and Distributed Computing: A Survey of Models, Paradigms and Approaches
Parallel and Distributed Computing: A Survey of Models, Paradigms and Approaches
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
A Case for NOW (Networks of Workstations)
IEEE Micro
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
Modeling Speedup (n) Greater than n
IEEE Transactions on Parallel and Distributed Systems
SCOOP: Solving Combinatorial Optimization Problems in Parallel
Solving Combinatorial Optimization Problems in Parallel - Methods and Techniques
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Annals of Mathematics and Artificial Intelligence
Problems of discrete optimization: Challenges and main approaches to solve them
Cybernetics and Systems Analysis
Weighted A∗ search -- unifying view and application
Artificial Intelligence
Finding optimal solutions to the twenty-four puzzle
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Best-first heuristic search for multicore machines
Journal of Artificial Intelligence Research
Search pruning techniques in SAT-based branch-and-bound algorithms for the binate covering problem
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Discrete optimization problems are interesting due to their complexity and applications, particularly in robotics. In this paper, a parallel algorithm that allows finding solutions to these problems, is presented. Then, the modifications that can be applied to it to obtain a second parallel algorithm that finds suboptimal solutions, reducing computation time, are studied. The algorithms proposed are based on two variations of the heuristic search algorithm Best First Search, and are called A* and Weighted A*, respectively. The parallel solutions were implemented using MPI to be run on cluster, taking the N2-1 Puzzle as study case. The experimental work focuses on analyzing the speedup and efficiency achieved for various initial instances, varying architecture configuration. Finally, the quality of the solutions found by the optimal and suboptimal algorithms are compared and performance variation is analyzed.