In this paper we merge two problem independent search strategies, namely Local Branching and Sliced Neighbor- hood Search. They both integrate CP tree search with local search concepts, but while local branching is very effective in exploring small neighborhoods, its performances decrease when dealing with diversification and large neighborhood ex- ploration. On the other hand Sliced Neighborhood Search is an effective method for exploring random slices of large neighborhoods and in moving away arbitrarily far from an incumbent solution. For this reason we obtain very good results in improving a reference solution: Local Branching obtains a 35% improvement when SNS is integrated aggres- sively both in the neighborhood exploration and in the di- versification strategy. The tests were conducted on large instances of the Travelling Salesman Problem with Time Windows.
Improving CP-based Local Branching via Sliced Neighborhood Search
MILANO, MICHELA
2011
Abstract
In this paper we merge two problem independent search strategies, namely Local Branching and Sliced Neighbor- hood Search. They both integrate CP tree search with local search concepts, but while local branching is very effective in exploring small neighborhoods, its performances decrease when dealing with diversification and large neighborhood ex- ploration. On the other hand Sliced Neighborhood Search is an effective method for exploring random slices of large neighborhoods and in moving away arbitrarily far from an incumbent solution. For this reason we obtain very good results in improving a reference solution: Local Branching obtains a 35% improvement when SNS is integrated aggres- sively both in the neighborhood exploration and in the di- versification strategy. The tests were conducted on large instances of the Travelling Salesman Problem with Time Windows.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
