Abstract
Interval Global Optimization based on Branch and Bound (B&B) technique is a standard for searching an optimal solution in the scope of continuous and discrete Global Optimization. It iteratively creates a search tree where each node represents a problem which is decomposed in several subproblems provided that a feasible solution can be found by solving this set of subproblems. The enormous computational power needed to solved most of the B&B Global Optimization problems and their high degree of parallelism make them suitable candidates to be solved in a multiprocessing environment. This work evaluates a parallel version of AMIGO (Advanced Multidimensional Interval Analysis Global Optimization) algorithm. AMIGO makes an efficient use of all the available information in continuous differentiable problems to reduce the search domain and to accelerate the search. Our parallel version takes advantage of the capabilities offered by Charm++. Preliminary results show our proposal as a good candidate to solve very hard global optimization problems.
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Martínez, J.A., Casado, L.G., Alvarez, J.A., García, I. (2006). Interval Parallel Global Optimization with Charm++. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558958_18
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DOI: https://doi.org/10.1007/11558958_18
Publisher Name: Springer, Berlin, Heidelberg
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