A non-diffident combinatorial optimization algorithm

Abstract

Branch and bound is a well known, all-purpose optimization strategy. It relies on the availability of a search space 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-diffident 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 low bound enables a faster convergence towards the best solution. The counterpart is that the search space might be traversed more than once. However, we show in an experimental evaluation that our approach remains beneficial.