74 International Journal of Applied Metaheuristic Computing, 2(2), 74-92, April-June 2011 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Design of Experiments, Hamiltonian Path, Iranian Cities, MetaheuristicAlgorithms, Traveling Salesman Problem DIMMA-Implemented Metaheuristics for Finding Shortest Hamiltonian Path Between Iranian Cities Using Sequential DOE Approach for Parameters Tuning Masoud Yaghini, Iran University of Science and Technology, Iran Mohsen Momeni, Iran University of Science and Technology, Iran Mohammadreza Sarmadi, Iran University of Science and Technology, Iran ABSTRACT 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 frst time, a comparative study on metaheuristic algorithms for fnding the shortest Hamiltonian path for 1071 Iranian cities is conducted. These are the main cities of Iran based on social-economic charac- teristics. For solving this problem, four hybrid effcient 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 algo- rithms, 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 effciency and effectiveness of the algorithms. 1. INTRODUCTION The shortest Hamiltonian path is a well-known and important combinatorial optimization problem. The goal of Traveling Salesman Problem (TSP) is to find the shortest path that visits each city in a given list exactly once and then returns to the starting city. The distances between n cities are stored in a distance matrix D with elements d ij where i, j = 1, 2, ..., n and DOI: 10.4018/jamc.2011040104