Relevant bibliographies by topics / Fuzzy optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Fuzzy optimization"

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Yari, Gholamhossein, and Mohamadtaghi Rahimi. "General solution for fuzzy portfolio optimization." International Journal of Academic Research 6, no. 6 (November 30, 2014): 220–26. http://dx.doi.org/10.7813/2075-4124.2014/6-6/a.28.

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Uehara, Kiyohiko, and Kaoru Hirota. "A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 2 (March 20, 2021): 213–25. http://dx.doi.org/10.20965/jaciii.2021.p0213.

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A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.
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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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Murat, Y. Sazi, and Shinya Kikuchi. "Fuzzy Optimization Approach." Transportation Research Record: Journal of the Transportation Research Board 2024, no. 1 (January 2007): 82–91. http://dx.doi.org/10.3141/2024-10.

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NASSERI, S. H. "FUZZY NONLINEAR OPTIMIZATION." Journal of Nonlinear Sciences and Applications 01, no. 04 (December 21, 2008): 230–35. http://dx.doi.org/10.22436/jnsa.001.04.05.

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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 (April 8, 2015): 57–65. http://dx.doi.org/10.5815/ijisa.2015.05.08.

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

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Revathi, M., Dr M. Valliathal, and R. Saravanan Dr K. Rathi. "A New Hendecagonal Fuzzy Number For Optimization Problems." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 326–31. http://dx.doi.org/10.31142/ijtsrd2258.

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Chen, Cheng-Hung, and Wen-Hsien Chen. "Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers." International Journal of Machine Learning and Computing 4, no. 5 (2014): 433–36. http://dx.doi.org/10.7763/ijmlc.2014.v4.450.

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

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Dissertations / Theses on the topic "Fuzzy optimization"

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Ruziyeva, Alina. "Fuzzy Bilevel Optimization." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2013. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-106378.

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In the dissertation the solution approaches for different fuzzy optimization problems are presented. The single-level optimization problem with fuzzy objective is solved by its reformulation into a biobjective optimization problem. A special attention is given to the computation of the membership function of the fuzzy solution of the fuzzy optimization problem in the linear case. Necessary and sufficient optimality conditions of the the convex nonlinear fuzzy optimization problem are derived in differentiable and nondifferentiable cases. A fuzzy optimization problem with both fuzzy objectives and constraints is also investigated in the thesis in the linear case. These solution approaches are applied to fuzzy bilevel optimization problems. In the case of bilevel optimization problem with fuzzy objective functions, two algorithms are presented and compared using an illustrative example. For the case of fuzzy linear bilevel optimization problem with both fuzzy objectives and constraints k-th best algorithm is adopted.
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Tada, Minoru. "STUDIES ON FUZZY COMBINATORIAL OPTIMIZATION." Kyoto University, 1994. http://hdl.handle.net/2433/160752.

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本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである
Kyoto University (京都大学)
0048
新制・論文博士
博士(工学)
乙第8724号
論工博第2927号
新制||工||976(附属図書館)
UT51-94-Z475
(主査)教授 茨木 俊秀, 教授 長谷川 利治, 教授 片山 徹
学位規則第4条第2項該当
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Dadone, Paolo. "Design Optimization of Fuzzy Logic Systems." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/27893.

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Fuzzy logic systems are widely used for control, system identification, and pattern recognition problems. In order to maximize their performance, it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Some data to approximate are commonly available and yield what is called the supervised learning problem. In this problem we typically wish to minimize the sum of the squares of errors in approximating the data. We first introduce fuzzy logic systems and the supervised learning problem that, in effect, is a nonlinear optimization problem that at times can be non-differentiable. We review the existing approaches and discuss their weaknesses and the issues involved. We then focus on one of these problems, i.e., non-differentiability of the objective function, and show how current approaches that do not account for non-differentiability can diverge. Moreover, we also show that non-differentiability may also have an adverse practical impact on algorithmic performances. We reformulate both the supervised learning problem and piecewise linear membership functions in order to obtain a polynomial or factorable optimization problem. We propose the application of a global nonconvex optimization approach, namely, a reformulation and linearization technique. The expanded problem dimensionality does not make this approach feasible at this time, even though this reformulation along with the proposed technique still bears a theoretical interest. Moreover, some future research directions are identified. We propose a novel approach to step-size selection in batch training. This approach uses a limited memory quadratic fit on past convergence data. Thus, it is similar to response surface methodologies, but it differs from them in the type of data that are used to fit the model, that is, already available data from the history of the algorithm are used instead of data obtained according to an experimental design. The step-size along the update direction (e.g., negative gradient or deflected negative gradient) is chosen according to a criterion of minimum distance from the vertex of the quadratic model. This approach rescales the complexity in the step-size selection from the order of the (large) number of training data, as in the case of exact line searches, to the order of the number of parameters (generally lower than the number of training data). The quadratic fit approach and a reduced variant are tested on some function approximation examples yielding distributions of the final mean square errors that are improved (i.e., skewed toward lower errors) with respect to the ones in the commonly used pattern-by-pattern approach. Moreover, the quadratic fit is also competitive and sometimes better than the batch training with optimal step-sizes, thus showing an improved performance of this approach. The quadratic fit approach is also tested in conjunction with gradient deflection strategies and memoryless variable metric methods, showing errors smaller by 1 to 7 orders of magnitude. Moreover, the convergence speed by using either the negative gradient direction or a deflected direction is higher than that of the pattern-by-pattern approach, although the computational cost of the algorithm per iteration is moderately higher than the one of the pattern-by-pattern method. Finally, some directions for future research are identified.
Ph. D.
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Arnett, Timothy J. "Iteratively Increasing Complexity During Optimization for Formally Verifiable Fuzzy Systems." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin156387481300899.

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Hu, Cheng Lin. "Design optimization of fuzzy models in system identification." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2493501.

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Lehar, Matthew A. 1977. "A branching fuzzy-logic classifier for building optimization." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32512.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.
Includes bibliographical references (p. 109-110).
We present an input-output model that learns to emulate a complex building simulation of high dimensionality. Many multi-dimensional systems are dominated by the behavior of a small number of inputs over a limited range of input variation. Some also exhibit a tendency to respond relatively strongly to certain inputs over small ranges, and to other inputs over very large ranges of input variation. A branching linear discriminant can be used to isolate regions of local linearity in the input space, while also capturing the effects of scale. The quality of the classification may be improved by using a fuzzy preference relation to classify input configurations that are not well handled by the linear discriminant.
by Matthew A. Lehar.
Ph.D.
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Gasir, Fathi Sidig. "Optimization of fuzzy regression trees using artificial immune systems." Thesis, Manchester Metropolitan University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574508.

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This thesis presents the development of a novel fuzzy regression tree algorithm known as Elgasir, which is based on the CHAID regression tree algorithm and Takagi-Sugeno fuzzy inference. The Elgasir Algorithm is applied to crisp regression trees to produce fuzzy regression trees in order to soften sharp decision boundaries inherited in crisp trees. Elgasir generates a fuzzy rule base by applying fuzzy techniques to crisp regression trees using Trapezoidal membership functions. Then Takagi-Sugeno fuzzy inference is used to aggregate the final output from the fuzzy implications. A novel optimization method based on the Artificial Immune Network model (opt-aiNet) is also proposed to optimize the fuzzification of fuzzy regression trees generated by the Elgasir Algorithm. Finally, the Elgasir Algorithm is developed further by proposing a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. A significant number of experiments were carried out for each of the proposed approaches, using five real-world regression problems from the VCI repository and KEEL repository. The empirical results have shown the effectiveness of using the proposed methods by increasing the prediction accuracy and robustness of fuzzy regression trees.
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Bahri, Oumayma. "A fuzzy framework for multi-objective optimization under uncertainty." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10030/document.

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Cette thèse est consacrée à l'étude de l’optimisation combinatoire multi-objective sous incertitudes. Plus particulièrement, nous abordons les problèmes multi-objectifs contenant des données floues qui sont exprimées par des nombres triangulaires floues. Pour faire face à ce type de problèmes, notre idée principale est d’étendre les concepts multi-objectifs classiques au contexte flou. Nous proposons, dans un premier temps, une nouvelle approche Pareto entre des objectifs flous (i.e. vecteurs des nombres triangulaires flous). Ensuite, nous étendons des méta-heuristiques basées sur Pareto afin de converger vers des solutions optimales floues. L’approche proposée est illustrée sur un problème bi-objectif de routage de véhicules avec des demandes floues. Dans le deuxième volet de ce travail, nous abordons l’aspect de robustesse dans le contexte multi-objectif flou en proposant une nouvelle méthodologie d’évaluation de robustesse des solutions. Finalement, les résultats expérimentaux sur des benchmarks flous du problème de routage de véhicules prouvent l’efficacité et la fiabilité de notre approche
This thesis is devoted to the study of multi-objective combinatorial optimization under uncertainty. In particular, we address multi-objective problems with fuzzy data, in which fuzziness is expressed by fuzzy triangular numbers. To handle such problems, our main idea is to extend the classical multi-objective concepts to fuzzy context. To handle such problems, we proposed a new Pareto approach between fuzzy-valued objectives (i.e. vectors of triangular fuzzy numbers). Then, an extension of Pareto-based metaheuristics is suggested as resolution methods. The proposed approach is thereafter illustrated on a bi-objective vehicle routing problem with fuzzy demands. At the second stage, we address robustness aspect in the multi-objective fuzzy context by proposing a new methodology of robustness evaluation of solutions. Finally, the experimental results on fuzzy benchmarks of vehicle routing problem prove the effectiveness and reliability of our approach
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Spence, William G. "An Optimization Approach To Employee Scheduling Using Fuzzy Logic." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/618.

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An Optimization Approach to Employee Scheduling Using Fuzzy Logic William G. Spence Selection of sales employees is critical because the sales employees represent the company’s image, competitive advantage, technology, and values. In many service systems the majority of consumer contact is with the sales department. Since there are different types of customers, scheduling quality salespersons who can adequately help consumers may affect revenue. This thesis proposes a new methodology for the scheduling of employees in a service system. The methodology uses Fuzzy Logic to calculate possible sales and Linear Programming to create an optimal schedule. This approach enables the rating of sales employees with respect to three customer’s types (Lookie Lou, Price Shopper and Buyer). The salesperson rating, along with customer arrival distribution is then used to optimize sale person scheduling, with the objective of revenue maximization. The uniqueness of this thesis lies in the combination of Fuzzy Logic and Linear Programming. The combination of these two disciplines provides an adaptive tool that can be used to optimize employee scheduling based on personality traits.
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Chiu, Kuan-Shiu. "Adaptive optimization of intelligent flow control." Thesis, University of Sunderland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288014.

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Books on the topic "Fuzzy optimization"

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Lodwick, Weldon A., and Janusz Kacprzyk, eds. Fuzzy Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2.

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Fedrizzi, Mario. Interactive Fuzzy Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.

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Wang, Shuming, and Junzo Watada. Fuzzy Stochastic Optimization. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5.

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Fang, Yong, Kin Keung Lai, and Shouyang Wang. Fuzzy Portfolio Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77926-1.

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Fedrizzi, Mario, Janusz Kacprzyk, and Marc Roubens, eds. Interactive Fuzzy Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-45700-5.

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Gupta, Pankaj, Mukesh Kumar Mehlawat, Masahiro Inuiguchi, and Suresh Chandra. Fuzzy Portfolio Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54652-5.

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Genetic algorithms and fuzzy multiobjective optimization. Boston: Kluwer Academic Publishers, 2002.

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Verdegay, José-Luis. Fuzzy sets based heuristics for optimization. Berlin: Springer, 2003.

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Keung, Lai Kin, and Wang Shouyang 1958-, eds. Fuzzy portfolio optimization: Theory and methods. Berlin: Springer, 2008.

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J, Jowers Leonard, and SpringerLink (Online service), eds. Monte Carlo Methods in Fuzzy Optimization. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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Book chapters on the topic "Fuzzy optimization"

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Dompere, Kofi Kissi. "Fuzziness, Rationality, Optimality and Equilibrium in Decision and Economic Theories." In Fuzzy Optimization, 3–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_1.

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Ogryczak, Włodzimierz, and Tomasz Śliwiński. "On Solving Optimization Problems with Ordered Average Criteria and Constraints." In Fuzzy Optimization, 209–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_10.

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Sirbiladze, Gia. "Fuzzy Dynamic Programming Problem for Extremal Fuzzy Dynamic System." In Fuzzy Optimization, 231–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_11.

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Mareš, Milan. "Vaguely Motivated Cooperation." In Fuzzy Optimization, 271–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_12.

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Kasperski, Adam, and Paweł Zieliński. "Computing Min-Max Regret Solutions in Possibilistic Combinatorial Optimization Problems." In Fuzzy Optimization, 287–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_13.

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Ge, Yue, and Hiroaki Ishii. "Stochastic Bottleneck Spanning Tree Problem on a Fuzzy Network." In Fuzzy Optimization, 313–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_14.

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Kuchta, Dorota. "The Use of Fuzzy Numbers in Practical Project Planning and Control." In Fuzzy Optimization, 323–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_15.

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Vieira, Susana M., João M. C. Sousa, and Uzay Kaymak. "Ant Feature Selection Using Fuzzy Decision Functions." In Fuzzy Optimization, 343–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_16.

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Tsuda, Hiroshi, and Seiji Saito. "Application of Fuzzy Theory to the Investment Decision Process." In Fuzzy Optimization, 365–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_17.

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Gil-Lafuente, Anna M., and José M. Merigó. "Decision Making Techniques in Political Management." In Fuzzy Optimization, 389–405. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_18.

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Conference papers on the topic "Fuzzy optimization"

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Chen, Min, and Simone A. Ludwig. "Fuzzy clustering using automatic particle swarm optimization." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891874.

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Krzeszowski, Tomasz, and Krzysztof Wiktorowicz. "Training Sparse Fuzzy Classifiers Using Metaheuristic Optimization." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494590.

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Chen, Xiangdong, and Junwei Li. "Robust Fuzzy Optimization." In 2008 International Conference on Computer Science and Information Technology (ICCSIT). IEEE, 2008. http://dx.doi.org/10.1109/iccsit.2008.114.

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Qun, Lin, Zhong Wenhui, and Wu Meijuan. "Fuzzy Optimization Design on Worm Drive." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0060.

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Abstract A mathematical model of multi-objective fuzzy optimization design is described in this paper, where the volume of corona dentis and the efficiency of transmission were chosen as objective functions. The optimal confidence level λ* and the augmentation coefficient β were specified by using two-level fuzzy synthetic evaluation method. As a consequence, the fuzzy optimization design problem was converted into non-fuzzy optimization design problem which could be solved with the mixed discrete optimization technique. The numerical result would be of some reference value to the design of worm drive.
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Kasperski, Adam, and Pawel Zielifiski. "On Possibilistic Combinatorial Optimization Problems." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630385.

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Gasir, Fathi, Zuhair Bandar, Keeley Crockett, and Alan Crispin. "Immune engineering for Elgasir algorithm optimization." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584190.

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Guillaume, Romain, Adam Kasperski, and Pawel Zielinski. "Robust Possibilistic Optimization with Copula Function." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494572.

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Guillaume, Romain, Adam Kasperski, and Pawel Zielinski. "Distributionally Robust Optimization in Possibilistic Setting." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494390.

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Kasperski, Adam, and Pawel Ziefinski. "Solving Combinatorial Optimization Problems with Fuzzy Weights." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630384.

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TRAN, Thi-To-Quyen, Thuong-Cang PHAN, Anne LAURENT, and Laurent D'ORAZIO. "Optimization for Large-Scale Fuzzy Joins Using Fuzzy Filters in MapReduce." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177610.

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Reports on the topic "Fuzzy optimization"

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Smith, James F., Rhyne III, and II Robert D. Fuzzy Logic Resource Management and Coevolutionary Game-based Optimization. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada390559.

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Dimitrov, Evgeni, Hayden Schaeffer, David Wen, Sandra Rankovic, Kizza Nandyose, and Olivier Thonnard. The Construction of a Vague Fuzzy Measure Through L1 Parameter Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada567409.

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Osipov, G. S. Optimization model of an inverse multi-stage problem with fuzzy matches. КультИнформПресс, 2018. http://dx.doi.org/10.18411/spbcsa-2018-9.

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