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Journal articles on the topic 'Optimization of fuzzy systemparameters'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

NASSERI, S. H. "FUZZY NONLINEAR OPTIMIZATION." Journal of Nonlinear Sciences and Applications 01, no. 04 (2008): 230–35. http://dx.doi.org/10.22436/jnsa.001.04.05.

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2

ISHII, Hiroaki. "Fuzzy Combinatiorial Optimization." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 31–40. http://dx.doi.org/10.3156/jfuzzy.4.1_31.

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3

Murat, Y. Sazi, and Shinya Kikuchi. "Fuzzy Optimization Approach." Transportation Research Record: Journal of the Transportation Research Board 2024, no. 1 (2007): 82–91. http://dx.doi.org/10.3141/2024-10.

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4

Dey, Samir, and Tapan Kumar Roy. "Multi-objective Structural Optimization Using Fuzzy and Intuitionistic Fuzzy Optimization Technique." International Journal of Intelligent Systems and Applications 7, no. 5 (2015): 57–65. http://dx.doi.org/10.5815/ijisa.2015.05.08.

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5

Ammar, Elsaid, and Joachim Metz. "On fuzzy convexity and parametric fuzzy optimization." Fuzzy Sets and Systems 49, no. 2 (1992): 135–41. http://dx.doi.org/10.1016/0165-0114(92)90319-y.

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6

Chen, T. Y., and C. C. Shieh. "Fuzzy multiobjective topology optimization." Computers & Structures 78, no. 1-3 (2000): 459–66. http://dx.doi.org/10.1016/s0045-7949(00)00091-2.

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7

Medaglia, Andrés L., Shu-Cherng Fang, and Henry L. W. Nuttle. "Fuzzy controlled simulation optimization." Fuzzy Sets and Systems 127, no. 1 (2002): 65–84. http://dx.doi.org/10.1016/s0165-0114(01)00153-1.

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8

Luhandjula, M. K., and M. M. Gupta. "On fuzzy stochastic optimization." Fuzzy Sets and Systems 81, no. 1 (1996): 47–55. http://dx.doi.org/10.1016/0165-0114(95)00240-5.

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9

Pedrycz, W., and J. V. de Oliveira. "Optimization of fuzzy models." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26, no. 4 (1996): 627–36. http://dx.doi.org/10.1109/3477.517038.

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10

Luhandjula, M. K. "Fuzzy optimization: An appraisal." Fuzzy Sets and Systems 30, no. 3 (1989): 257–82. http://dx.doi.org/10.1016/0165-0114(89)90019-5.

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11

Wang, Changyou, Dong Qiu, and Yonghong Shen. "Fuzzy Number, Fuzzy Difference, Fuzzy Differential: Theory and Applications." Axioms 14, no. 4 (2025): 254. https://doi.org/10.3390/axioms14040254.

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12

Shirin, Shapla, and Kamrunnahar. "Application of Fuzzy Optimization Problem in Fuzzy Environment." Dhaka University Journal of Science 62, no. 2 (2015): 119–25. http://dx.doi.org/10.3329/dujs.v62i2.21976.

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In this paper application of optimization problem has been introduced which belong to fuzzy environment. An attempt has been taken to find out a suitable option in order to obtain the optimum solutions of optimization problems in fuzzy environment. Optimum solutions of the proposed optimization problem computed by using three methods, such as Bellman-Zadehs method, Zimmermans Method, and Fuzzy Version of Simplex method, are compared to each other. In support of that the three algorithms of the above three methods have been reviewed. However, the main objective of this paper is to focus on th

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13

Buşoniu, Lucian, Damien Ernst, Bart De Schutter, and Robert Babuška. "Fuzzy Partition Optimization for Approximate Fuzzy Q-iteration." IFAC Proceedings Volumes 41, no. 2 (2008): 5629–34. http://dx.doi.org/10.3182/20080706-5-kr-1001.00949.

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14

Budnitzki, Alina. "Linear optimization with fuzzy variable over fuzzy polytope." Journal of Intelligent & Fuzzy Systems 29, no. 2 (2015): 499–507. http://dx.doi.org/10.3233/ifs-141225.

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15

Umamaheswari, P., and K. Ganesan. "Fuzzy Unconstrained Optimization Problems with Triangular Fuzzy Numbers." IOP Conference Series: Materials Science and Engineering 912 (September 12, 2020): 062048. http://dx.doi.org/10.1088/1757-899x/912/6/062048.

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16

Zhang, Bingqing. "Logistics Transportation Time Optimization Based on Fuzzy Particle Swarm Optimization." MATEC Web of Conferences 359 (2022): 01024. http://dx.doi.org/10.1051/matecconf/202235901024.

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The fuzzy particle swarm optimization (FPSO-RRB- algorithm is used to solve the logistics transportation time optimization problem, a three-dimensional particle coding method based on receiving point, particle position sequence and particle position rounding operation is proposed, the results are compared with those of fuzzy particle swarm optimization algorithm. The experimental results show that the fuzzy particle swarm optimization algorithm can effectively optimize the logistics transportation time.

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17

Wei, Jing-xuan, and Yu-ping Wang. "Fuzzy Particle Swarm Optimization for Constrained Optimization Problems." Journal of Electronics & Information Technology 30, no. 5 (2011): 1218–21. http://dx.doi.org/10.3724/sp.j.1146.2007.00689.

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18

Kaya, Ersin, Alper Kılıç, İsmail Babaoğlu, and Ahmet Babalık. "FUZZY ADAPTIVE WHALE OPTIMIZATION ALGORITHM FOR NUMERIC OPTIMIZATION." Malaysian Journal of Computer Science 34, no. 2 (2021): 184–98. http://dx.doi.org/10.22452/mjcs.vol34no2.4.

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Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the

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19

BURGIN, MARK. "FUZZY OPTIMIZATION OF REAL FUNCTIONS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 04 (2004): 471–97. http://dx.doi.org/10.1142/s021848850400293x.

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The main goal of this paper is to develop such means of analysis that allows us to reflect and model vagueness and uncertainty of our knowledge, which result from imprecision of measurement and inaccuracy of computation. To achieve this goal, we use here neoclassical analysis to problems of optimization. Neoclassical analysis extends the scope and results of the classical mathematical analysis by applying fuzzy concepts to conventional mathematical objects, such as functions, sequences, and derivatives. Basing on the theory of fuzzy limits, we construct a fuzzy extension for the classical theo

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20

E., M., and A. Salama. "Optimization Model By Fuzzy Environment." International Conference on Mathematics and Engineering Physics 4, no. 4 (2008): 1–13. http://dx.doi.org/10.21608/icmep.2008.29890.

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21

Luhandjula, Monga K. "On Fuzzy Random-Valued Optimization." American Journal of Operations Research 01, no. 04 (2011): 259–67. http://dx.doi.org/10.4236/ajor.2011.14030.

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22

Wang Yingfen. "Fuzzy Optimization Method in Classification." Journal of Convergence Information Technology 6, no. 6 (2011): 231–34. http://dx.doi.org/10.4156/jcit.vol6.issue6.23.

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23

Sarosa, M., A. S. Ahmad, B. Riyanto, and A. S. Noer. "Optimization of Neuro-Fuzzy System." ITB Journal of Information and Communication Technology 1, no. 1 (2007): 56–69. http://dx.doi.org/10.5614/itbj.ict.2007.1.1.5.

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24

Lodwick, Weldon A., and K. David Jamison. "Interval Methods and Fuzzy Optimization." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 03 (1997): 239–49. http://dx.doi.org/10.1142/s0218488597000221.

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In this paper, we describe interval-based methods for solving constrained fuzzy optimization problems. The class of fuzzy functions we consider for the optimization problems is the set of real-valued functions where one or more parameters/coefficients are fuzzy numbers. The focus of this research is to explore some relationships between fuzzy set theory and interval analysis as it relates to optimization problems.

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25

MIRANDA, PEDRO, and MICHEL GRABISCH. "OPTIMIZATION ISSUES FOR FUZZY MEASURES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 06 (1999): 545–60. http://dx.doi.org/10.1142/s0218488599000477.

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In this paper, we address the problem of identification of fuzzy measures through different representations, namely the Möbius, the Shapley and the Banzhaf interaction representations. In the first part of the paper, we recall the main results concerning these representations, and give a simple algorithm to compute them. Then we determine the bounds of the Möbius and the interaction representations for fuzzy measures. Lastly, the identification of fuzzy measures by minimizing a quadratic error criterion is addressed. We give expressions of the quadratic program for all the considered represent

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26

Sutikno, Tole. "Fuzzy optimization and metaheuristic algorithms." Babylonian Journal of Mathematics 2023 (November 20, 2023): 59–65. http://dx.doi.org/10.58496/bjm/2023/012.

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Fuzzy optimization and metaheuristic algorithms are two important fields in computational intelligence. Fuzzy optimization deals with the optimization of systems or processes that involve fuzzy sets or fuzzy logic, while metaheuristic algorithms are a class of optimization algorithms that are designed to solve difficult problems by mimicking natural processes. In this paper, we present a review of fuzzy optimization and metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing. We discuss the advantages and limitations

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27

Ramík, Jaroslav. "Extension principle in fuzzy optimization." Fuzzy Sets and Systems 19, no. 1 (1986): 29–35. http://dx.doi.org/10.1016/s0165-0114(86)80075-6.

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28

Fu Guoyao. "Optimization methods for fuzzy clustering." Fuzzy Sets and Systems 93, no. 3 (1998): 301–9. http://dx.doi.org/10.1016/s0165-0114(96)00227-8.

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29

Gobi, A. F., and W. Pedrycz. "Fuzzy modelling through logic optimization." International Journal of Approximate Reasoning 45, no. 3 (2007): 488–510. http://dx.doi.org/10.1016/j.ijar.2006.06.026.

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30

Marano, Giuseppe Carlo, and Giuseppe Quaranta. "Fuzzy-based robust structural optimization." International Journal of Solids and Structures 45, no. 11-12 (2008): 3544–57. http://dx.doi.org/10.1016/j.ijsolstr.2008.02.016.

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31

Hanebeck, Uwe D., and Günther K. Schmidt. "Genetic optimization of fuzzy networks." Fuzzy Sets and Systems 79, no. 1 (1996): 59–68. http://dx.doi.org/10.1016/0165-0114(95)00291-x.

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32

Yi-Cherng, Yeh, and Hsu Deh-Shiu. "Structural optimization with fuzzy parameters." Computers & Structures 37, no. 6 (1990): 917–24. http://dx.doi.org/10.1016/0045-7949(90)90005-m.

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33

Luhandjula, M. K. "Fuzzy optimization: Milestones and perspectives." Fuzzy Sets and Systems 274 (September 2015): 4–11. http://dx.doi.org/10.1016/j.fss.2014.01.004.

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34

ČERNÝ, MARTIN. "FUZZY APPROACH TO VECTOR OPTIMIZATION." International Journal of General Systems 20, no. 1 (1991): 23–29. http://dx.doi.org/10.1080/03081079108945010.

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35

Kikuchi, Shinya, Nopadon Kronprasert, and Said M. Easa. "Aggregate Blending Using Fuzzy Optimization." Journal of Construction Engineering and Management 138, no. 12 (2012): 1411–20. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000557.

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36

Robati, Amir, Gholam Abbas Barani, Hossein Nezam Abadi Pour, Mohammad Javad Fadaee, and Javad Rahimi Pour Anaraki. "Balanced fuzzy particle swarm optimization." Applied Mathematical Modelling 36, no. 5 (2012): 2169–77. http://dx.doi.org/10.1016/j.apm.2011.08.006.

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37

Tan, Sie-Keng, and Pei-Zhuang Wang. "A Characterization of Optimal Fuzzy Sets in Fuzzy Optimization." Journal of Intelligent and Fuzzy Systems 1, no. 4 (1993): 313–17. http://dx.doi.org/10.3233/ifs-1993-1406.

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38

Rani Mathew, Edithstine, and Lovelymol Sebastian. "FUZZY OPTIMIZATION AND gH- SYMMETRICALLY DERIVATIVE OF FUZZY FUNCTIONS." jnanabha 52, no. 02 (2022): 272–79. http://dx.doi.org/10.58250/jnanabha.2022.52232.

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In this paper, we introduce a new concept called Algebra of generalized Hukuhara symmetrically (gHs) differentiable fuzzy function. We specifically state the prerequisites for the gHs differentiability of the product and composition of a differentiable real function and a gHs differentiable fuzzy function, as well as the gHs differentiability of the sum of two gHs differentiable fuzzy functions.

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39

Lodwick, Weldon A., and K. David Jamison. "A Constraint Fuzzy Interval Analysis approach to fuzzy optimization." Information Sciences 426 (February 2018): 38–49. http://dx.doi.org/10.1016/j.ins.2017.10.026.

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40

R. Mahalakshmi, M. Ananthanarayanan,. "Optimization of Fuzzy Sequencing Problems with Heptagonal Fuzzy Numbers." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 5817–24. http://dx.doi.org/10.52783/tjjpt.v44.i4.1988.

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Consider the situation of planning 5 occupations to 4 machines, the handling time as Heptagonal fuzzy nos. The fuzzy sequencing numbers is modified into crisp value by utilizing ‘Python’ program. Consequently the ideal succession of the positions is found by using Johnson’s Bellman‘s Algorithm, in which total elapsed time and idle time for each machine is obtained.

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41

Zou, Tingting. "Extended Duality in Fuzzy Optimization Problems." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/826752.

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Duality theorem is an attractive approach for solving fuzzy optimization problems. However, the duality gap is generally nonzero for nonconvex problems. So far, most of the studies focus on continuous variables in fuzzy optimization problems. And, in real problems and models, fuzzy optimization problems also involve discrete and mixed variables. To address the above problems, we improve the extended duality theory by adding fuzzy objective functions. In this paper, we first define continuous fuzzy nonlinear programming problems, discrete fuzzy nonlinear programming problems, and mixed fuzzy no

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42

Sheibani, Kaveh. "Fuzzy Greedy Search." International Journal of Applied Management Sciences and Engineering 4, no. 2 (2017): 1–12. http://dx.doi.org/10.4018/ijamse.2017070101.

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This paper presents mathematics of the so-called fuzzy greedy evaluation concept which can be integrated into approaches for hard combinatorial optimization problems. The proposed method evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism, thereby exploiting a fuzzy solution space using greedy methods. Given that the greedy algorithms are computationally inexpensive compared to other more sophisticated methods for combinatorial optimization; this shows practical significance of using the proposed approach. The effectiveness and efficiency of the proposed method are

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43

Zhu, Qiang, and Per-Åke Larson. "A Fuzzy Query Optimization Approach for Multidatabase Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 06 (1997): 701–22. http://dx.doi.org/10.1142/s0218488597000518.

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A crucial challenge for global query optimization in a multidatabase system (MDBS) is that some local optimization information, such as local cost parameters, may not be accurately known at the global level because of local autonomy. Traditional query optimization techniques using a crisp cost model may not be suitable for an MDBS because precise information is required. In this paper we present a new approach that performs global query optimization using a fuzzy cost model that allows fuzzy information. We suggest methods for establishing a fuzzy cost model and introduce a fuzzy optimization

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44

Bernal, Emer, Oscar Castillo, José Soria, and Fevrier Valdez. "Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment." Axioms 8, no. 1 (2019): 26. http://dx.doi.org/10.3390/axioms8010026.

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Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobi

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45

Lavanya. K. "Compartive Reliability Analysis of a System with Linear and Non-linear Membership Functions using (3,2)-Fuzzy Sets." Advances in Nonlinear Variational Inequalities 28, no. 4s (2025): 572–80. https://doi.org/10.52783/anvi.v28.3513.

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In this paper, we propose a multi-objective non-linear reliability global optimization and system cost as two objective functions. As a generalized version of fuzzy set, intuitionistic fuzzy set, Pythagorean fuzzy set, (3,2)-fuzzy set is a very useful tool to express uncertainty, impreciseness in more general way. we have considered (3,2)-fuzzy optimization technique with linear and non-linear membership function to solve this multi-objective reliability optimization model. To demonstrate the methodology and applicability of the proposed approach, numerical examples are presented and evaluated

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46

Guo, Lina. "Optimization of Regional Industrial Structure Based on Multiobjective Optimization and Fuzzy Set." Mathematical Problems in Engineering 2022 (June 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/4006967.

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With the development of China’s economy, it is required to change the mode of economic growth, from extensive growth mode to intensive growth mode. Therefore, the research on industrial structure is of great significance. This study proposes a regional industrial structure optimization method based on multiobjective optimization and fuzzy set. Firstly, the multiobjective industrial structure evaluation model is constructed, then the evaluation model based on improved fuzzy industrial structure is proposed, and finally the application effect of improved fuzzy industrial structure evaluation mod

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47

Mashchenko, Sergey O., and Mohammed Saad Ibrahim Al-Sammarraie. "An Optimization Problem with a Fuzzy Set of Fuzzy Constraints." Journal of Automation and Information Sciences 46, no. 8 (2014): 38–48. http://dx.doi.org/10.1615/jautomatinfscien.v46.i8.50.

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48

Li, Chen, Gong Zeng-tai, and Duan Gang. "Genetic Algorithm Optimization for Determining Fuzzy Measures from Fuzzy Data." Journal of Applied Mathematics 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/542153.

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Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is a very difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms, neural networks, and particle swarm algorithm, it is hard to say which one is more appropriate and more feasible. Each method has its advantages. Most of the existed works can only deal with the data consisting of classic numbers which may arise limitations in practical applications. It is not reas

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49

Ghosh, Debdas. "On identifying fuzzy knees in fuzzy multi-criteria optimization problems." SeMA Journal 76, no. 2 (2018): 343–64. http://dx.doi.org/10.1007/s40324-018-0179-8.

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50

Hemalatha, S., and K. Annadurai. "OPTIMIZATION OF A FUZZY INVENTORY MODEL WITH PENTAGONAL FUZZY NUMBERS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 03 (2023): 3277–87. http://dx.doi.org/10.47191/ijmcr/v11i3.01.

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This paper explores an optimal replenishment strategy for a two-echelon inventory model which has been considered and analyzed in a fuzzy environment. In fuzzy environment, carrying cost, ordering cost and the replenishment processing cost are assumed to be pentagonal fuzzy numbers. The purpose of this model is to minimize the total inventory cost in fuzzy scenario. There are two inventory models proposed in this paper. Crisp models are developed with fuzzy total inventory cost but crisp optimal order quantity. Fuzzy model is also formulated with fuzzy total inventory cost and fuzzy optimal or

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