The corridor method: a dynamic programming inspired metaheuristic

This paper presents a dynamic programming inspired metaheuristic called Corridor Method. It can be classified as a method-based iterated local search in that it deploys method-based neighbourhoods. By this it is meant that the search for a new candidate solution is carried out by a fully-fledged optimization method and generates a global optimal solution over the neighbourhood. The neighbourhoods are thus constructed to be suitable domains for the fully-fledged optimization method used. Typically, these neighbourhoods are obtained by the imposition of exogenous constraints on the decision space of the target problem and therefore must be compatible with the optimization method used to search these neighbourhoods. This is in sharp contrast to traditional metaheuristics where neighbourhoods are move-based, i.e., they are generated by subjecting the candidate solution to small changes called moves. While conceptually this method-based paradigm applies to any optimization method, in practice it is best suited to support optimization methods such as dynamic programming, where it is easy to control the size of a problem, hence the complexity of algorithms, by means of exogenous constraints. The essential features of the Corridor Method are illustrated by a number of examples, including the travelling salesman problem, where exponentially large neighbourhoods are searched by a linear time/space dynamic programming algorithm.