Neighborhood Portfolio Approach for Local Search Applied to Timetabling Problems

A recent trend in local search concerns the exploitation of several different neighborhoods so as to increase the ability of the algorithm to navigate the search space. In this work we investigate a hybridization technique, that we call Neighborhood Portfolio Approach, that consists in the interleave of local search techniques based on various combinations of neighborhoods. In particular, we are able to select the most effective search technique through a systematic analysis of all meaningful combinations built upon a set of basic neighborhoods. The proposed approach is applied to two practical problems belonging to the timetabling family, and systematically tested and compared on real-world instances. The experimental analysis shows that our approach leads to automatic design of new algorithms that provide better results than basic local search techniques.