An Iterated Local Search Approach for Finding Provably Good Solutions for Very Large TSP Instances
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A New Three-Level Tree Data Structure for Representing TSP Tours in the Lin-Kernighan Heuristic
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Markovian search games in heterogeneous spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A multi-inner-world genetic algorithm using multiple heuristics to optimize delivery schedule
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Artificial Life and Robotics
Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times
Journal of Computational and Applied Mathematics
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem
INFORMS Journal on Computing
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This correspondence describes a hybrid genetic algorithm (GA) to find high-quality solutions for the traveling salesman problem (TSP). The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics. It uses a variant of the maximal preservative crossover and the double-bridge move mutation. An effective implementation of the Lin-Kernighan heuristic (LK) is incorporated into the method to compensate for the GA's lack of local search ability. The method is validated by comparing it with the LK-Helsgaun method (LKH), which is one of the most effective methods for the TSP. Experimental results with benchmarks having up to 316 228 cities show that the proposed method works more effectively and efficiently than LKH when solving large-scale problems. Finally, the method is used together with the implementation of the iterated LK to find a new best tour (as of June 2, 2003) for a 1 904 711-city TSP challenge