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Tytuł artykułu

A Multi-criteria fuzzy random crop planning problem using evolutionary optimization

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Sixth International Conference on Research in Intelligent and Computing
Języki publikacji
EN
Abstrakty
EN
In this paper, we deal with fuzzy random objectives in a multi-criteria crop planning problem considered as a multi-objective linear programming problem. These fuzzy random factors are related to decision making processes in practice, especially the uncertainty and synthesized objectives of experts. The problem is transformed into a multi-objective nonlinear programming problem by a step of evaluating expectation value. Instead of using classical methods, we use a multi-objective evolutionary algorithms called NSGA-II to solve the equivalent problem. This helps finding many approximate solutions concurrently with a low time consumption. In computational experiments, we create a specific fuzzy random crop planning problem with the data synthesized from government's reports and show convergence of the algorithm for proposed model.
Rocznik
Tom
Strony
49--52
Opis fizyczny
Bibliogr. 22 poz., tab., wykr.
Twórcy
  • School of Applied Mathematics and Informatics Hanoi University of Science and Technology, Hanoi, Vietnam
  • School of Applied Mathematics and Informatics Hanoi University of Science and Technology, Hanoi, Vietnam
  • School of Applied Mathematics and Informatics Hanoi University of Science and Technology, Hanoi, Vietnam
Bibliografia
  • 1. S. Fidanova, M. Ganzha, O. Roeva, “InterCriteria Analyzis of Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem,” 16th Conference on Computer Science and Intelligence Systems, vol. 25, 2021, pp.173-180.
  • 2. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, 2002, pp.182-197.
  • 3. Z. Du, K. Chen, “Enhanced Artificial Bee Colony with Novel Search Strategy and Dynamic Parameter,” Computer Science and Information Systems, vol. 16, no. 3, 2019, pp. 939-957.
  • 4. S. Garcia, C. Trinh, “Comparison of Multi-objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis,” in Processes, vol. 7, 2019, p. 361.
  • 5. A.O. Hamadameen, Z. M. Zainuddin, “Multiobjective fuzzy stochastic linear programming problems in the 21st century,” Life Science Journal, vol. 10, 2013, pp. 616-647.
  • 6. W. Huang, Y. Zhang, L. Li, “Survey on Multi-Objective Evolutionary Algorithms,” Journal of Physics: Conference Series, vol. 1288, 2019.
  • 7. R. Jain, L. Malangmeih, S. S. Raju, S. K. Srivastava, K. Immaneulraj, A. P. Kaur, “Optimization techniques for crop planning: A review,” Indian Journal of Agricultural Sciences, vol. 88, 2018, pp. 1826-1835.
  • 8. H. Katagiri, M. Sakawa, H. Ishii, “Multiobjective fuzzy random linear programming using E-model and probability measure,” Joint 9-th IFSA World Congress and 20-th NAFIPS International Conference, vol. 4, 2001, pp. 2295-2300.
  • 9. M. K. Luhandjula, M. M. Gupta, “On fuzzy stochastic optimization,” Fuzzy Sets and Systems, vol. 81, 1996, pp. 47-55.
  • 10. S. A. Mortazavi, R. Hezareh, S. A. Kaliji, S. S. Mehr, “Application of Linear and Non-linear Programming Model to Assess the Sustainability of Water Resources in Agricultural Patterns,” International Journal of Agricultural Management and Development, vol. 4, 2014, pp. 27-32.
  • 11. J. L. Pachuau, A. Roy, G. Krishna, H. K. Saha, “Estimation of traffic matrix from links load using genetic algorithm,” Scalable Computing: Practice and Experience, vol. 22, no. 1, 2021, pp. 29-38.
  • 12. N. Riquelme, C. V. Lücken, B. Baran, “Performance metrics in multiobjective optimization,” Latin American Computing Conference, 2015, pp. 1-11.
  • 13. M. Sakawa, “Fuzzy Sets and Interactive Multiobjective Optimization,” in Plenum, 1993.
  • 14. J. Soltani, A. R. Karbasi, S. M. Fahimifard, “Determining optimum cropping pattern using Fuzzy Goal Programming (FGP) model,” African Journal of Agricultural Research, vol. 6, 2011, pp. 3305-3310.
  • 15. T. N. Thang, D. T. Luc, N. T. B. Kim, “Solving generalized convex multiobjective programming problems by a normal direction method,” A Journal of Mathematical Programming and Operations Research, vol. 65, 2016, pp. 2269-2292.
  • 16. T. N. Thang, V. K. Solanki, D. T. Anh, N. T. N. Anh, P. V. Hai, “A monotonic optimization approach for solving strictly quasiconvex multiobjective programming problems,” Journal of Intelligent & Fuzzy Systems, vol. 38, 2020, pp. 6053-6063.
  • 17. T. Toyonaga, T. Itoh, H. Ishii, “A Crop Planning Problem with Fuzzy Random Profit Coefficients,” Fuzzy Optimization and Decision Making, vol. 4, 2005, pp. 51-69.
  • 18. S. Vajda, “Probabilistic Programming,” Academic Press, 1972.
  • 19. Q. Xu, Z. Xu, T. Ma, “A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions,” IEEE Access, vol. 8, 2020, pp. 41588-41614.
  • 20. H. Yano, K. Matsui, M. Furuhashi, “Multiobjective fuzzy random linear programming problems based on coefficients of variation,” International Journal of Applied Mathematics, vol. 44, 2014, pp. 137-143.
  • 21. H. Yano, M. Sakawa, “Interactive fuzzy decision making for multiobjective fuzzy random linear programming problems and its application to a crop planning problem,” Computational Intelligence, vol. 577, 2015, pp. 143-157.
  • 22. H. Yano, “Interactive Multiobjective Decision Making Under Uncertainty,” CRC Press, 2017.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-65cd6b60-233f-49b5-a4ca-2c2e9f5b0b81
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