We consider the neighborhood tree induced by alternating the use of different
neighborhood structures within a local search descent. We investigate the issue
of designing a search strategy operating at the neighborhood tree level by
exploring different paths of the tree in a heuristic way. We show that allowing
the search to 'backtrack' to a previously visited solution and resuming the
iterative variable neighborhood descent by 'pruning' the already explored
neighborhood branches leads to the design of effective and efficient search
heuristics. We describe this idea by discussing its basic design components
within a generic algorithmic scheme and we propose some simple and intuitive
strategies to guide the search when traversing the neighborhood tree. We
conduct a thorough experimental analysis of this approach by considering two
different problem domains, namely, the Total Weighted Tardiness Problem
(SMTWTP), and the more sophisticated Location Routing Problem (LRP). We show
that independently of the considered domain, the approach is highly
competitive. In particular, we show that using different branching and
backtracking strategies when exploring the neighborhood tree allows us to
achieve different trade-offs in terms of solution quality and computing cost.Comment: Genetic and Evolutionary Computation Conference (GECCO'12) (2012
