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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References
Ahmed, A.M., Rashid, T.A., Saeed, S.A.M.: Cat swarm optimization algorithm: a survey and performance evaluation. Comput. Intell. Neurosci. 2020 (2020)
Chu, S. C., Tsai, P. W., Pan, J. S. Cat swarm optimization. In: Pacific Rim International Conference on Artificial Intelligence (pp. 854–858). Springer, Berlin, Heidelberg (2006)
Fouskakis, D., Draper, D.: Stochastic optimization: a review. Int. Stat. Rev. 70(3), 315–349 (2002)
Harper, M., Anderson, B., James, P., Bahaj, A.: Assessing socially acceptable locations for onshore wind energy using a GIS-MCDA approach. Int. J. Low-Carbon Technol. 14(2), 160–169 (2019)
Heyman, D. P., Sobel, M. J. Stochastic Models in Operations Research: Stochastic Optimization, Vol. 2. Courier Corporation (2004)
Hu, X., Eberhart, R.: Solving constrained nonlinear optimization problems with particle swarm optimization. In: Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics, Vol. 5, pp. 203–206. Citeseer (2002)
Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: International fuzzy systems association world congress (pp. 789–798). Springer, Berlin, Heidelberg (2007)
Wątróbski, J., Jankowski, J., Ziemba, P.: Multistage performance modelling in digital marketing management. Econ. Sociol. 9(2), 101 (2016)
Kizielewicz, B., Kołodziejczyk, J.: Effects of the selection of characteristic values on the accuracy of results in the COMET method. Procedia Comput. Sci. 176, 3581–3590 (2020)
Kizielewicz, B., Sałabun, W.: A new approach to identifying a multi-criteria decision model based on stochastic optimization techniques. Symmetry 12(9), 1551 (2020)
Kizielewicz, B., Wątróbski, J., Sałabun, W.: Identification of relevant criteria set in the MCDA process-wind farm location case study. Energies 13(24), 6548 (2020)
Kizielewicz, B., Dobryakova, L.: MCDA based approach to sports players’ evaluation under incomplete knowledge. Procedia Comput. Sci. 176, 3524–3535 (2020)
Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)
Murtza, S.A., Ahmad, A., Shafique, J.: Integer cat swarm optimization algorithm for multiobjective integer problems. Soft. Comput. 24(3), 1927–1955 (2020)
Sałabun, W.: The characteristic objects method: a new distance-based approach to multicriteria decision-making problems. J. Multi-Criteria Decis. Anal. 22(1–2), 37–50 (2015)
Sałabun, W., Piegat, A.: Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome. Artif. Intell. Rev. 48(4), 557–571 (2017)
Sałabun, W., Wątróbski, J., Piegat, A.: Identification of a multi-criteria model of location assessment for renewable energy sources. In: International Conference on Artificial Intelligence and Soft Computing (pp. 321–332). Springer, Cham (2016)
Sałabun, W., Ziemba, P., Wątróbski, J. The rank reversals paradox in management decisions: The comparison of the ahp and comet methods. In: International Conference on Intelligent Decision Technologies, pp. 181–191. Springer, Cham (2016)
Schneider, J., & Kirkpatrick, S.: Stochastic Optimization. Springer Science & Business Media (2007)
Sharafi, Y., Khanesar, M. A., Teshnehlab, M.: Discrete binary cat swarm optimization algorithm. In: 2013 3rd IEEE International Conference on Computer, Control and Communication (IC4), pp. 1–6. IEEE (2013)
Shekhovtsov, A., Kołodziejczyk, J., Sałabun, W.: Fuzzy model identification using monolithic and structured approaches in decision problems with partially incomplete data. Symmetry 12(9), 1541 (2020)
Qin, X.S., Huang, G.H., Sun, W., Chakma, A.: Optimization of remediation operations at petroleum-contaminated sites through a simulation-based stochastic-MCDA approach. Energy Sourc. Part A 30(14–15), 1300–1326 (2008)
Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A., Zioło, M.: Generalised framework for multi-criteria method selection. Omega 86, 107–124 (2019)
Wątróbski, J., ałabun, W. Green supplier selection framework based on multi-criteria decision-analysis approach. In: International Conference on Sustainable Design and Manufacturing, pp. 361–371. Springer, Cham (2016)
Wątróbski, J., Sałabun, W., Karczmarczyk, A., Wolski, W.: Sustainable decision-making using the COMET method: An empirical study of the ammonium nitrate transport management. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 949–958. IEEE (2017)
Więckowski, J., Kizielewicz, B., Kołodziejczyk, J.: The search of the optimal preference values of the characteristic objects by using particle swarm optimization in the uncertain environment. In: International Conference on Intelligent Decision Technologies, pp. 353–363. Springer, Singapore (2020)
Więckowski, J., Kizielewicz, B., Kołodziejczyk, J.: Application of hill climbing algorithm in determining the characteristic objects preferences based on the reference set of alternatives. In: International Conference on Intelligent Decision Technologies, pp. 341–351. Springer, Singapore (2020)
Więckowski, J., Kizielewicz, B., Kołodziejczyk, J. Finding an Approximate Global Optimum of Characteristic Objects Preferences by Using Simulated Annealing. In International Conference on Intelligent Decision Technologies, pp. 365–375. Springer, Singapore (2020)
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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