A tabu search approach to optimization of drilling operations
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Depth-First Branch-and-Bound versus Local Search: A Case Study
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
New Models and Heuristics for Component Placement in Printed Circuit Board Assembly
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
Embedded local search approaches for routing optimization
Computers and Operations Research
Evolution of Fitness Functions to Improve Heuristic Performance
Learning and Intelligent Optimization
Combinations of local search and exact algorithms
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times
Journal of Computational and Applied Mathematics
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
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In this paper we present effective new local and variable neighbourhood search heuristics for the asymmetric Travelling Salesman Problem. Our local search approach, HyperOpt, is inspired by a heuristic developed for a sequencing problem arising in the manufacture of printed circuit boards. In our approach we embed an exact algorithm into a local search heuristic in order to exhaustively search promising regions of the solution space. We propose a hybrid of HyperOpt and 3-opt which allows us to benefit from the advantages of both approaches and gain better tours overall. Using this hybrid within the Variable Neighbourhood Search (VNS) metaheuristic framework, as suggested by Hansen and Mladenovic, allows us to overcome local optima and create tours of very high quality. We introduce the notion of a "guided shake" within VNS and show that this yields a heuristic which is more effective than the random shakes proposed by Hansen and Mladenovic. The heuristics presented form a continuum from very fast ones which produce reasonable results to much slower ones which produce excellent results. All of the heuristics have proven capable of handling the sort of constraints which arise for real life problems, such as those in electronics assembly.