Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
A branch and bound algorithm for the traveling salesman and the transportation routing problems
Computers and Industrial Engineering
Crossover on intensive search and traveling salesman problem
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Parallel simulated annealing algorithms
Journal of Parallel and Distributed Computing
Tabu Search
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Design and Analysis of Experiments
Design and Analysis of Experiments
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
Computers and Operations Research
Natural Computation for the Traveling Salesman Problem
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 01
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Computers and Operations Research
Estimation-based metaheuristics for the probabilistic traveling salesman problem
Computers and Operations Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Applied Metaheuristic Computing
DOE-based parameter tuning for local branching algorithm
International Journal of Metaheuristics
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A Hamiltonian path is a path in an undirected graph, which visits each node exactly once and returns to the starting node. Finding such paths in graphs is the Hamiltonian path problem, which is NP-complete. In this paper, for the first time, a comparative study on metaheuristic algorithms for finding the shortest Hamiltonian path for 1071 Iranian cities is conducted. These are the main cities of Iran based on social-economic characteristics. For solving this problem, four hybrid efficient and effective metaheuristics, consisting of simulated annealing, ant colony optimization, genetic algorithm, and tabu search algorithms, are combined with the local search methods. The algorithms' parameters are tuned by sequential design of experiments (DOE) approach, and the most appropriate values for the parameters are adjusted. To evaluate the proposed algorithms, the standard problems with different sizes are used. The performance of the proposed algorithms is analyzed by the quality of solution and CPU time measures. The results are compared based on efficiency and effectiveness of the algorithms.