Abstract
A new derivative-free global optimization algorithm is proposed for solving nonlinear global optimization problems. It is based on the Branch and Bound (BnB) algorithm. BnB is a general algorithm to solve optimization problems. Its implementation is done by using the developed template library of BnB algorithms. The robustness of the new algorithm is demonstrated by solving a selection of test problems. We present a short description of our template implementation of the BnB algorithm. A paradigm of domain decomposition (data parallelization) is used to construct a parallel BnB algorithm. MPI is used for underlying communications. To obtain a better load balancing, the BnB template has a load balancing module that allows the redistribution of a search space among the processors at a run time. A parallel version of the user’s algorithm is obtained automatically from a sequential algorithm.
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Čiegis, R., Baravykaitė, M. (2007). Implementation of a Black-Box Global Optimization Algorithm with a Parallel Branch and Bound Template. In: Kågström, B., Elmroth, E., Dongarra, J., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2006. Lecture Notes in Computer Science, vol 4699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75755-9_129
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DOI: https://doi.org/10.1007/978-3-540-75755-9_129
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