Work load balance approaches for branch and bound algorithms on distributed systems

Branch and Bound is a standard method 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 computational power needed to solve most of the Branch and Bound Global Optimization problems and their high degree of potential parallelism make them suitable candidates to be solved in a multiprocessing environment. With parallel processing in mind Branch and Bound techniques can be considered as irregular and dynamic problems. So, their parallel implementations are not straightforward and require the use of dynamic load balance methods where the workload of a subproblem is a crucial parameter. In this paper an efficient parallel approach to the Branch and Bound continuous Global Optimization problem is described. It is based on a centralized asynchronous parallel model and on the prediction of the work loan of the set of subproblems containing a feasible solution. The proposed dynamic load balancing model obtains an almost perfect work load balance with low communication overhead.