Abstract
The randomness of data appears in many problems in various fields. Stochastic optimization methods are often used to solve such problems. However, a large number of methods developed makes it difficult to determine which method is the optimal choice for solving a given problem. In this paper, the cat swarm optimization (CSO) was used to find the optimal preference values of characteristic objects, which were then subjected to applying the characteristic objects method (COMET). The determined problem was solved using the randomly chosen training and testing sets, where both were subjected to two criteria. The study’s motivation was to analyze the effectiveness of the CSO algorithm compared to other stochastic methods in solving problems of a similar class. The obtained solution shows that the used algorithm can be effectively applied to the defined problem, noting much better results than previously tested methods.
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Acknowledgements
The work was supported by the project financed within the framework of the program of the Minister of Science and Higher Education under the name “Regional Excellence Initiative” in the years 2019–2022, Project Number 001/RID/2018/19; the amount of financing: PLN 10.684.000,00 (J.W.).
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Więckowski, J., Shekhovtsov, A., Wątróbski, J. (2021). A New Approach to Identifying of the Optimal Preference Values in the MCDA Model: Cat Swarm Optimization Study Case. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_22
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DOI: https://doi.org/10.1007/978-981-16-2765-1_22
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