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A Bi-Objetive Cat Swarm Optimization Algorithm for Set Covering Problem

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Artificial Intelligence Perspectives in Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 464))

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Abstract

In this paper, we study a classical problem in combinatorics and computer science, Set Covering Problem. It is one of Karp’s 21 NP-complete problems, using a new and original metaheuristic, Cat Swarm Optimization. This algorithm imitates the domestic cat through two states: seeking and tracing mode. The OR-Library of Beasley instances were used for the benchmark with additional fitness function, thus the problem was transformed from Mono-objective to Bi-objective. The Cat Swarm Optimization finds a set solution non-dominated based on Pareto concepts, and an external file for storing them. The results are promising for further continue in future work optimizing this problem.

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References

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Acknowledgments

The author Broderick Crawford is supported by grant CONICYT/FONDECYT/REGULAR/1140897 and Ricardo Soto is supported by grant CONICYT/FONDECYT/INICIACION/11130459

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Correspondence to Hugo Caballero .

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Crawford, B., Soto, R., Caballero, H., Olguín, E. (2016). A Bi-Objetive Cat Swarm Optimization Algorithm for Set Covering Problem. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-33625-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-33625-1_44

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  • Publisher Name: Springer, Cham

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