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Journal of Optimization
Volume 2017, Article ID 8042436, 7 pages
Research Article

Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms

1Brazilian Institute for Space Research (INPE), Cachoeira Paulista, SP, Brazil
2Universidade Estadual Paulista (UNESP), Guaratinguetá, SP, Brazil

Correspondence should be addressed to Eduardo Batista de Moraes Barbosa; rb.epni@asobrab.odraude

Received 10 February 2017; Accepted 10 April 2017; Published 7 June 2017

Academic Editor: Ferrante Neri

Copyright © 2017 Eduardo Batista de Moraes Barbosa and Edson Luiz França Senne. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters looking for candidate configurations close to a promising alternative. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach.