Abstract
The Set Covering Problem (SCP) consists in finding a set of solutions that allow to cover a set of necessities with the minor possible cost. There are many applications of this problem such as rolling production lines or installation of certain services like hospitals. SCP has been solved before with different algorithms like genetic algorithm, cultural algorithm or firefly algorithm among others. The objective of this paper is to show the performance of an Artificial Fish Swarm Algorithm (AFSA) in order to solve SCP. This algorithm, simulates the behavior of a fish shoal inside water and it uses a population of points in space to represent the position of a fish in the shoal. Here we show a study of its simplified version of AFSA in a binary domain with its modifications applied to SCP. This method was tested on SCP benchmark instances from OR-Library website.
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Acknowledgements
Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1140897. Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455. Sebastián Mansilla Villablanca, Álvaro Gómez, Adrián Jaramillo and Juan Salas are supported by Postgraduate Grant Pontificia Universidad Católica de Valparaíso 2016 (INF-PUCV 2016).
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Crawford, B. et al. (2016). An Artificial Fish Swarm Optimization Algorithm to Solve Set Covering Problem. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_76
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