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
Integer Programming Formulation of Traveling Salesman Problems
Journal of the ACM (JACM)
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Design and Analysis of Experiments
Design and Analysis of Experiments
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Computers and Industrial Engineering
Computers and Operations Research
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Tuning the performance of the MMAS heuristic
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Computers and Operations Research
Estimation-based metaheuristics for the probabilistic traveling salesman problem
Computers and Operations Research
International Journal of Applied Metaheuristic Computing
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Design of experiments (DOE) refers to a process of planning the experiments so that appropriate data that can be analysed by statistical methods will be collected, resulting in valid and objective conclusions. This paper presents a DOE-based approach for parameter tuning of local branching algorithm. Local branching is a metaheuristic technique utilising a general MIP solver to explore neighbourhoods. This solution strategy is exact in nature, although it is designed to improve heuristic behaviour of MIP solver at hand. The proposed approach has been applied to find shortest Hamiltonian path in travelling salesman problem (TSP). A Hamiltonian path is a path in an undirected graph, which visits each node exactly once, and returns to the starting node. To evaluate the algorithm, the standard problems with different sizes are used. The performance of the algorithm is analysed by the quality of solution and CPU time.