A Subpath Ejection Method for the Vehicle Routing Problem
Management Science
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
A new branch-and-cut algorithm for the capacitated vehicle routing problem
Mathematical Programming: Series A and B
Active guided evolution strategies for large-scale vehicle routing problems with time windows
Computers and Operations Research
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Generating SAT local-search heuristics using a GP hyper-heuristic framework
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Hyperheuristic for the parameter tuning of a bio-inspired algorithm of query routing in p2p networks
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems
Fundamenta Informaticae - Emergent Computing
An improved choice function heuristic selection for cross domain heuristic search
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Evolutionary hyperheuristic for capacitated vehicle routing problem
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper we present a hill-climbing based hyperheuristic which is able to solve instances of the capacitated vehicle routing problem. The hyperheuristic manages a sequence of constructive-perturbative pairs of low-level heuristics which are applied sequentially in order to construct and improve partial solutions. We present some design considerations that we have taken into account to find the most promising sequence and allow the collaboration among low-level heuristics. Our approach has been tested using some standard state-of-the-art benchmarks and we have compared them with several well-known methods proposed in the literature. We have obtained, on average, stable and good quality solutions after solving various types of problems. Thus, we conclude that our collaborative framework is an interesting approach as it has proved to be: (1) able to adapt itself to different problem instances by choosing a suitable combination of low-level heuristics and (2) capable of preserving stability when solving different types of problems.