A Hyper-Heuristic Based on An Adapter Layer for Transportation Combinatorial Problems

Hyper-heuristics are optimization techniques for solving hard combinatorial problems. Their main feature is that their design involves an important decoupling of the search components from the problem domain ones. This allows them to extend their applicability to different problem domains without major redesign, unlike traditional methods such as metaheuristics. In this work, a hyper-heuristic is evaluated for a transportation problem. The implemented hyper-heuristic uses a greedy operator, and it implements an adapter layer that would allow it to be used in other similar problems. Experimental results shows balanced solution quality and CPU time performance, regarding other metaheuristics in literature.