EVALUATION OF A PARALLEL BRANCH-AND-BOUND ALGORITHM ON A CLASS OF MULTIPROCESSORS

In this paper, we propose and evaluate a parallel ''decomposite best-f irst'' search branch-and-bound algorithm (dbs) for MIN-based multiproc essor systems. We start with a new probabilistic model to estimate the number of evaluated nodes for a serial best-first search branch-and-b ound algorithm. This analysis is used in predicting the parallel algor ithm speed-up. The proposed algorithm initially decomposes a problem i nto N subproblems, where N is the number of processors available in a multiprocessor. Afterwards, each processor executes the serial best-fi rst search to find a local feasible solution. Local solutions are broa dcasted through the network to compute the final solution. A conflict- free mapping scheme, known as the step-by-step spread, is used for sub problem distribution on the MIN. A speedup expression for the parallel algorithm is then derived using the serial best-first search node eva luation model. Our analysis considers both computation and communicati on overheads for providing realistic speed-up. Communication modeling is also extended for the parallel global best-first search technique. All the analytical results are validated via simulation. For large sys tems, when communication overhead is taken into consideration, it is o bserved that the parallel decomposite best-first search algorithm prov ides better speed-up compared to other reported schemes.