Extremal Optimization Applied to the Minimum Order Frequency Assignment Problem



Frequency assignment problem, Computational intelligence, Evolutionary computation, Extremal optimization, Meta-heuristic algoritms


Extremal Optimization (EO) is a metaheuristic used to solve complex combinatorial optimization problems. However, the behavior of this algorithm has not been studied for various interesting problems. Such as, it is the case of the Minimum Order Frequency Assignment Problem (MO-FAP). Specifically, this problem seeks to minimize the number of frequencies assigned to a group of transceiver, without violating any of the problem’s constraints. In particular, the aim of this study was to use EO to solve the MO-FAP. Furthermore, it was found that EO was able to find the best reported value for most instances of the dataset used for experimentation. Additionally, it was possible to identify how the EO Tau hyperparameter reported better results for much higher values than those reported in the literature. Finally, the difficulty of defining and configuring a fitness function for each element of the solution was evidenced. Therefore, this can affect the effectiveness of the algorithm according to the particular characteristics of the dataset to be solved. Consequently, this opens an opportunity to develop improvements in the form of defining the contribution of each element of the solution to the objective function.


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Author Biographies

Pedro Gomez-Meneses, Universidad Católica de la Santísima Concepción, Concepción, Biobío, 4090541, Chile.

Pedro Gomez-Meneses received his B. Sc. degree in Engineering, B. Eng. in Informatic Civil Engineering, PG. Dip. in Industrial Engineering from Universidad de Concepción, Chile. He received his Ph.D. degree in Information Technology from Bond University, Australia. He is currently assistant professor in the Department of Informatic Engineering at the Universidad Católica de la Santísima Concepción (UCSC), Chile. He has been Head of the Informatic Civil Engineering Career and Head of the Department of Informatic Engineering at the UCSC. His research interests are in the area of nature-inspired computation, metaheuristic search methods, combinatorial optimization and machine learning.

Héctor Ayala, Universidad Católica de la Santísima Concepción, Concepción, Biobío, 4090541, Chile.

Héctor Ayala received his B.Sc. degree in Engineering, and Informatic Civil Engineer from the Universidad Católica de la Santísima Concepción, Concepción, Chile. He has experience in database and machine learning. His research interests are machine learning, combinatorial optimization and metaheuristic methods.


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How to Cite

Gomez-Meneses, P., & Ayala, H. (2023). Extremal Optimization Applied to the Minimum Order Frequency Assignment Problem. IEEE Latin America Transactions, 21(3), 466–474. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7527