Analytical modeling of a parallel branch-and-bound algorithm on MIN-based multiprocessors

The authors propose a parallel decomposite, best-first` search branch-and bound algorithm for MIN-based multiprocessors. They start with a new probabilistic model to estimate the number of evaluated nodes for a serial algorithm. The proposed algorithm initially decomposes a problem into several subproblems. Each processor executes the serial best-first search to find a local feasible solution. The local solutions are broadcast through the network to compute the final solution. The speed-up analysis considers both the computation and communication overheads. It is seen that the parallel decomposite best-first search algorithm performs better than other reported schemes when communication overhead is taken into consideration