A Non-Diffident Combinatorial Optimization Algorithm

Branch and bound is a well known, all-purpose optimization strategy. It relies on the availability of a search tree evaluation function and maintains an upper bound cost as a reference for the pruning. This upper bound is updated each time a better solution is encountered. In this paper, we propose a non-diident optimization strategy that imposes this value. The computation of the bound is based on some heuristics that use probabilistic knowledge of the problem. Setting an arbitrary upper bound enables a faster convergence towards the best solution. The counterpart is that the search tree might be traversed more than once. However, we show in an experimental evaluation that our approach remains beneecial.