Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Applying evolutionary programming to selected traveling salesman problems
Cybernetics and Systems
The ant colony optimization meta-heuristic
New ideas in optimization
Fitness landscapes and memetic algorithm design
New ideas in optimization
Future Generation Computer Systems
Repair and Brood Selection in the Traveling Salesman Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
New Genetic Local Search Operators for the Traveling Salesman Problem
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Street-Based Routing Using an Evolutionary Algorithm
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
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The Traveling Salesman Problem is a standard test-bed for algorithmic ideas. Currently, there exists a large number of nature-inspired algorithms for the TSP and for some of these approaches very good performance is reported. In particular, the best performing approaches combine solution modification or construction with the subsequent application of a fast and effective local search algorithm. Yet, comparisons between these algorithms with respect to performance are often difficult due to different implementation choices of which the one of the local search algorithm is particularly critical. In this article we experimentally compare some of the best performing recently proposed nature-inspired algorithms which improve solutions by using a same local search algorithm and investigate their performance on a large set of benchmark instances.