Implementation of a black-box global optimization algorithm with a parallel Branch and Bound template

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. © Springer-Verlag Berlin Heidelberg 2007.