Abstract
In industry, the concept of complex systems is becoming relevant due to the diverse applications in operations research. Many of these complex problems are NP-hard and it is difficult to approach them with complete optimization techniques. The use of metaheuristics has had good results and in particular, the design of binary algorithms based on continuous metaheuristics of swarm intelligence. In this article, we apply the binarization mechanism based on the percentile concept. We apply the percentile concept to the sine-cosine algorithm (SCOA) in order to solve the multidimensional backpack problem (MKP). The experiments are designed to demonstrate the usefulness of the percentile concept in binarization. In addition, we verify the efficiency of our algorithm through reference instances. The results indicate that the binary Percentile Sine-Cosine Optimization Algorithm (BPSCOA) obtains adequate results when evaluated with a combinatorial problem such as the MKP.
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Pinto, H., Peña, A., Valenzuela, M., Fernández, A. (2019). A Binary Sine-Cosine Algorithm Applied to the Knapsack Problem. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_13
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DOI: https://doi.org/10.1007/978-3-030-19810-7_13
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