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Journal articles on the topic 'Multiobjective genetic fuzzy systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Vijayakumar, K. "Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/962402.

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Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sortin

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Shahsavari Pour, N., H. Asadi, and M. Pour Kheradmand. "Fuzzy Multiobjective Traffic Light Signal Optimization." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/249726.

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Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the

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Antonelli, Michela, Pietro Ducange, and Francesco Marcelloni. "Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach." IEEE Transactions on Fuzzy Systems 20, no. 2 (2012): 276–90. http://dx.doi.org/10.1109/tfuzz.2011.2173582.

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Kiyota, T., Y. Tsuji, and E. Kondo. "Unsatisfying functions and multiobjective fuzzy satisficing design using genetic algorithms." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 33, no. 6 (2003): 889–97. http://dx.doi.org/10.1109/tsmcb.2003.810899.

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Zolfagharian, Ali, Amin Noshadi, Seyed Ebrahim Ghasemi, and Mohd Zarhamdy Md Zain. "A nonparametric approach using artificial intelligence in vibration and noise reduction of flexible systems." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 8 (2013): 1329–47. http://dx.doi.org/10.1177/0954406213508510.

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The main aim of this paper is to broaden the application’s area of artificial intelligence including fuzzy logic and multiobjective evolutionary algorithm into real-time control area. Wiper system is a high order, nonlinear model with single-input and multi-outputs so that rise time, maximum overshoot, and end-point vibration of wiper blade are observed in conflict as the faster response leads to the larger level of undesired noise and vibration. The first part of this paper centers acquiring experimental data from a passenger automobile wiper system during its operation and using a reliable n

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Trebi-Ollennu, A., and B. A. White. "Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design." IEE Proceedings - Control Theory and Applications 144, no. 2 (1997): 137–42. http://dx.doi.org/10.1049/ip-cta:19971031.

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Sakawa, Masatoshi, and Kosuke Kato. "An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures." Advances in Operations Research 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/372548.

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We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization. By considering the vague nature of the decision maker's judgments, fuzzy goals of the decision maker are introduced, and the problem is interpreted as maximizing an overall degree of satisfaction with the multiple fuzzy goals. For deriving a satisficing solution for the decision maker, we develop an interactive fuzzy satisficing method. Realizing the block-angular structures that can be exploited in solving pr

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Sakawa, Masatoshi, and Ryo Kubota. "Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms." European Journal of Operational Research 120, no. 2 (2000): 393–407. http://dx.doi.org/10.1016/s0377-2217(99)00094-6.

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Ying-Yi Hong and Wei-Fen Huang. "Interactive multiobjective passive filter planning with fuzzy parameters in distribution systems using genetic algorithms." IEEE Transactions on Power Delivery 18, no. 3 (2003): 1043–50. http://dx.doi.org/10.1109/tpwrd.2003.813630.

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Zhang, Limei, Honglei Yang, Jing Lv, Yongfu Liu, and Wei Tang. "Multiobjective Optimization Approach for Coordinating Different DG from Distribution Network Operator." Journal of Electrical and Computer Engineering 2018 (November 15, 2018): 1–13. http://dx.doi.org/10.1155/2018/4790942.

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Integrating with analysis of uncertainties, this paper presented a multiobjective optimization approach for coordinating different DG from the perspective of Distribution Network Operator (DISOPER). Aiming to three uncertain factors including fuzzy variable, random variable, and interval variable, the information entropy and interval analysis methods are adopted to construct multistate models of multisource uncertainty. The information entropy method is to convert fuzzy variable into equivalent random variable. Interval analysis method is to transform random variables into interval variables b

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Casillas, Jorge, and Francisco J. Martínez-López. "Mining uncertain data with multiobjective genetic fuzzy systems to be applied in consumer behaviour modelling." Expert Systems with Applications 36, no. 2 (2009): 1645–59. http://dx.doi.org/10.1016/j.eswa.2007.11.035.

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Sakawa, M., and K. Yauchi. "An interactive fuzzy satisficing method for multiobjective nonconvex programming problems with fuzzy numbers through coevolutionary genetic algorithms." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 31, no. 3 (2001): 459–67. http://dx.doi.org/10.1109/3477.931546.

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Ishibuchi, Hisao, and Yusuke Nojima. "Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning." International Journal of Approximate Reasoning 44, no. 1 (2007): 4–31. http://dx.doi.org/10.1016/j.ijar.2006.01.004.

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Mohamed El-Saeed, Menna Allah E., Amal F. Abdel-Gwaad, and Mohamed A. Farahat. "Capacitor Allocation Using Multiobjective Water Cycle Algorithm and Fuzzy Logic." Elektronika ir Elektrotechnika 28, no. 2 (2022): 35–45. http://dx.doi.org/10.5755/j02.eie.30355.

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Radial distribution systems are susceptible to a lack of voltage profile and increase system losses, particularly at the distant ends of the distribution feeder. This manuscript proposes an approach to solve the optimal capacitor placement problem in radial distribution networks to minimize system losses, improve the voltage profile of all buses, promote total voltage stability, and improve net savings. The optimal capacitor placement problem is solved in two stages. Firstly, normalized loss sensitivity factor and voltage magnitude are used as inputs to build fuzzy expert rules to arrange the

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Liu, Xiaojuan, and Jian’an Fang. "Long-Term Load Forecasting Based on a Time-Variant Ratio Multiobjective Optimization Fuzzy Time Series Model." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/781043.

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Load forecasting problem is a complex nonlinear problem linked with economic and weather factors. Long-term load forecasting provides useful information for maintenance scheduling, adequacy assessment, and limited energy resources for electrical power systems. Fuzzy time series forecasting models can be used for long-term load forecasting. However, the interval length has been chosen arbitrarily in the implementations of known fuzzy time series forecasting models, which has an important impact on the performance of these models. In this paper, a time-variant ratio multiobjective optimization f

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KIYOTA, Takanori, Yasutaka TSUJI, Masashi NODA, and Eiji KONDO. "A Design Method of Type-1 Servo Systems by Multiobjective Fuzzy Satisficing Approach Using Genetic Algorithms." Transactions of the Japan Society of Mechanical Engineers Series C 70, no. 696 (2004): 2392–98. http://dx.doi.org/10.1299/kikaic.70.2392.

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Kyomugisha, Rebeccah, Christopher Maina Muriithi, and George Nyauma Nyakoe. "Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm." Journal of Electrical and Computer Engineering 2022 (April 29, 2022): 1–22. http://dx.doi.org/10.1155/2022/7456333.

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The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational

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18

Gorzałczany, Marian B., and Filip Rudziński. "Detection and Localization of False Data Injection Attacks in Smart Grids Applying an Interpretable Fuzzy Genetic Machine Learning/Data Mining Approach." Energies 18, no. 7 (2025): 1568. https://doi.org/10.3390/en18071568.

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In this paper, we consider the problem of accurate, transparent, and interpretable detection, as well as the localization of false data injection attacks (FDIAs) in smart grids. In order to address that problem, we employ our knowledge discovery machine learning/data mining (ML/DM) approach—implemented as a collection of fuzzy rule-based classifiers (FR-BCs)—characterized by a genetically optimized accuracy–interpretability trade-off. Our approach uses our generalization (showing better performance) of the well-known SPEA2 method to carry out the genetic learning and multiobjective optimizatio

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19

Kato, Kosuke, and Masatoshi Sakawa. "An interactive fuzzy satisficing method for large scale multiobjective 0–1 programming problems with fuzzy parameters through genetic algorithms." European Journal of Operational Research 107, no. 3 (1998): 590–98. http://dx.doi.org/10.1016/s0377-2217(97)00157-4.

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20

Ozdemir Dag, G., and Mustafa Bagriyanik. "A Parallel Implementation of Unscheduled Flow Control in Interconnected Power Systems." Mathematical Problems in Engineering 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/376291.

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The unscheduled power flow problem needs to be minimized or controlled as soon as possible in a deregulated power system since the transmission systems are mostly operated at their power-carrying limits or very close to it. The time spent for simulations to determine the current states of all the system and control variables of the interconnected power system is important. Taking necessary action in case of any failure of equipment or any other occurrence of an undesired situation could be critical. Using supercomputing facilities and parallel computing techniques together decreases the comput

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21

Sakawa, Masatoshi, and Katsuhiro Yauchi. "An interactive fuzzy satisficing method for multiobjective nonconvex programming problems through floating point genetic algorithms." European Journal of Operational Research 117, no. 1 (1999): 113–24. http://dx.doi.org/10.1016/s0377-2217(98)00128-3.

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22

Saravanan, R., S. Ramabalan, and C. Balamurugan. "Multiobjective trajectory planner for industrial robots with payload constraints." Robotica 26, no. 6 (2008): 753–65. http://dx.doi.org/10.1017/s0263574708004359.

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SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select

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23

Shafiei, Allahkaram, Mohammad (Behdad) Jamshidi, Farzad Khani, et al. "A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing." Mathematical Problems in Engineering 2021 (October 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/9194578.

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Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized s

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Jiang, Yanjun, Jianguo Jiang, and Yankui Zhang. "A Novel Fuzzy Multiobjective Model Using Adaptive Genetic Algorithm Based on Cloud Theory for Service Restoration of Shipboard Power Systems." IEEE Transactions on Power Systems 27, no. 2 (2012): 612–20. http://dx.doi.org/10.1109/tpwrs.2011.2179951.

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Faouzi, Titel, and Belarbi Khaled. "A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretability of a Neuro-Fuzzy Controller." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2614–26. https://doi.org/10.11591/ijece.v7i5.pp2614-2626.

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In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpr

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26

Marquez, Antonio A., Francisco A. Marquez, Ana M. Roldan, and Antonio Peregrin. "An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling." Knowledge-Based Systems 54 (December 5, 2013): 42–52. https://doi.org/10.1016/j.knosys.2013.05.012.

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The use of adaptive connectors as conjunction operators in adaptive fuzzy inference systems is one of the methodologies, also compatible with others, to improve the accuracy of fuzzy rule-based systems by means of local adaptation of the inference process to each rule of the rule base. However, when dealing with such currently challenging issues as high-dimensional regression problems, adapting their parameters becomes difficult due to the exponential rule explosion. In this paper, we propose to address the problem by using a new adaptive conjunction operator. This operator provides considerab

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Zhang, Yudong, Shuihua Wang, and Genlin Ji. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications." Mathematical Problems in Engineering 2015 (2015): 1–38. http://dx.doi.org/10.1155/2015/931256.

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algor

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Onisawa, Takehisa, and Sadaaki Miyamoto. "Applications of Soft Computing to Human-centered Information Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 1 (1999): 1–2. http://dx.doi.org/10.20965/jaciii.1999.p0001.

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Soft computing was advocated by Prof. Zadeh as a total technology complementary to the advantages and disadvantages of fuzzy theory, neural network models, genetic algorithms, and so on - a wide variety of topics covered at scientific conferences, in books, in papers, etc. In human-centered information systems, human beings play a central role in information processing. Human information processing involves uncertainty, fuzziness, ambiguity, subjectivity, etc., be dealt with well by soft computing. Human-centered information processing systems are important fields of soft computing. This speci

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Srilakshmi, Koganti, Gummadi Srinivasa Rao, Katragadda Swarnasri, et al. "Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization." International Transactions on Electrical Energy Systems 2024 (March 19, 2024): 1–21. http://dx.doi.org/10.1155/2024/6611240.

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This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony sea

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Dong, Qing, and Yanjun Wu. "Research on Risk Assessment of Multitarget Wireless Sensing Detection Auxiliary Engineering in Mine Geological Environment." Journal of Sensors 2021 (September 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/9915430.

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In this paper, we evaluate the risk of auxiliary engineering of mine geological environment utilizing multiobjective wireless sensing detection. After vectorization of each evaluation factor, we evaluate the geological environment of a high-top mountain mine by fuzzy comprehensive evaluation method and get the evaluation results of our geological environment. Firstly, an in-depth analysis is carried out for the tracking gate that most association methods pay little attention to. In terms of optimization of traditional tracking gate, an attempt is made to propose a genetic algorithm fusion codi

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Hazrati, Hanif, Abbas Barzegarinegad, and Hamid Siaby-Serajehlo. "A Hybrid Mathematical and Decision-Making Model to Determine the Amount of Economic Order considering the Discount." Mathematical Problems in Engineering 2021 (December 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/5229949.

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Suppliers are one of the most important parts of the supply chain, whose performance indirectly has a significant impact on customer satisfaction. Because customer demands are different from organizations, organizations have to consider different criteria for selecting their suppliers. In recent years, many studies in this field have been conducted using various criteria and methods. The main purpose defined in this research is to develop a model for simultaneous item ordering systems in real business conditions. In this research, a model is developed by considering the two objectives of minim

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Yang, W., and D. Z. Sun. "A genetic algorithm-based fuzzy multi-objective programming approach for environmental water allocation." Water Supply 6, no. 5 (2006): 43–50. http://dx.doi.org/10.2166/ws.2006.763.

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A fuzzy multiobjective-programming model was presented to determine appropriate environmental flow allocation in urban rivers. An integrated nonlinear fuzzy membership function, that represents a broader implication in fuzzy decision-making, was regarded as the general objective and genetic algorithm became an essential tool in search for the global optimal solution. A case study of optimal allocation for environmental water requirements in the Liming River Basin, Daqing, demonstrates the capability of the fuzzy multiobjective model based on fuzzy decision theories to work suitably in water en

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Dong, Ze, Hao Jia, and Miao Liu. "An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/6123874.

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This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vo

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SAKAWA, Masatoshi, Masahiro INUIGUCHI, Hideaki SUNADA, and Kazuya SAWADA. "Fuzzy Multiobjective Combinatorial Optimization through Revised Genetic Algorithms." Journal of Japan Society for Fuzzy Theory and Systems 6, no. 1 (1994): 177–86. http://dx.doi.org/10.3156/jfuzzy.6.1_177.

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SAKAWA, Masatoshi, and Ryo KUBOTA. "Fuzzy Programming for Multiobjective Fuzzy Job Shop Scheduling Problems through Genetic Algorithms." Journal of Japan Society for Fuzzy Theory and Systems 11, no. 5 (1999): 781–88. http://dx.doi.org/10.3156/jfuzzy.11.5_77.

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Reardon, Brian J. "Fuzzy logic versus niched Pareto multiobjective genetic algorithm optimization." Modelling and Simulation in Materials Science and Engineering 6, no. 6 (1998): 717–34. http://dx.doi.org/10.1088/0965-0393/6/6/004.

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Saggiani, G. M., G. Caligiana, and F. Persiani. "Multiobjective wing design using genetic algorithms and fuzzy logic." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 218, no. 2 (2004): 133–45. http://dx.doi.org/10.1243/0954410041321961.

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Mukhopadhyay, A., U. Maulik, and S. Bandyopadhyay. "Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes." IEEE Transactions on Evolutionary Computation 13, no. 5 (2009): 991–1005. http://dx.doi.org/10.1109/tevc.2009.2012163.

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Bica, B., A. J. Chipperfield, and P. J. Fleming. "Fuzzy Model Identification by Means of Multiobjective Genetic Programming." IFAC Proceedings Volumes 33, no. 24 (2000): 93–98. http://dx.doi.org/10.1016/s1474-6670(17)36874-x.

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Kelesoglu, Omer. "Fuzzy multiobjective optimization of truss-structures using genetic algorithm." Advances in Engineering Software 38, no. 10 (2007): 717–21. http://dx.doi.org/10.1016/j.advengsoft.2007.03.003.

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Huang, Wei, and Sung-Kwun Oh. "Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/725017.

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We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA). The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effecti

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Tahriri, Farzad, Siti Zawiah Md Dawal, and Zahari Taha. "Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA." Scientific World Journal 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/505207.

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A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parame

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Wu, Hsien-Chung. "Using Genetic Algorithms and Core Values of Cooperative Games to Solve Fuzzy Multiobjective Optimization Problems." Axioms 13, no. 5 (2024): 298. http://dx.doi.org/10.3390/axioms13050298.

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A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is a conventional optimization problem. Usually, the assignments of suitable coefficients to the corresponding scalar optimization problem are subjectively determined by the decision makers. However, these assignments may cause some biases by their subjectivity. Therefore, this paper proposes a mech

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Wang, Zu-Tong, Jian-Sheng Guo, Ming-Fa Zheng, and Ying Wang. "A New Hybrid Intelligent Algorithm for Fuzzy Multiobjective Programming Problem Based on Credibility Theory." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/909203.

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Based on the credibility theory, this paper is devoted to the fuzzy multiobjective programming problem. Firstly, the expected-value model of fuzzy multiobjective programming problem is provided based on credibility theory; then two new approaches for obtaining efficient solutions are proposed on the basis of the expected-value model, whose validity has been proven. For solving the fuzzy MOP problem efficiently, Latin hypercube sampling, fuzzy simulation, support vector machine, and artificial bee colony algorithm are integrated to build a hybrid intelligent algorithm. An application case study

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Jia, Li-Min, and Xi-Di Zhang. "On fuzzy multiobjective optimal control." Engineering Applications of Artificial Intelligence 6, no. 2 (1993): 153–64. http://dx.doi.org/10.1016/0952-1976(93)90031-r.

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Pei, Pei, Ser Tong Quek, and Yongbo Peng. "Multiobjective Reliability-Based Design Optimization of the Fuzzy Logic Controller for MR Damper-Based Structures." Structural Control and Health Monitoring 2023 (August 14, 2023): 1–26. http://dx.doi.org/10.1155/2023/4009397.

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To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic

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Chao, Chun-Tang, Ming-Tang Liu, Chi-Jo Wang, and Juing-Shian Chiou. "A fuzzy adaptive controller for cuckoo search algorithm in active suspension system." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 3 (2018): 761–71. http://dx.doi.org/10.1177/1461348418811473.

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This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the

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Regattieri Delgado, Myriam, Fernando Von Zuben, and Fernando Gomide. "Hierarchical genetic fuzzy systems." Information Sciences 136, no. 1-4 (2001): 29–52. http://dx.doi.org/10.1016/s0020-0255(01)00140-2.

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Carse, B., and A. G. Pipe. "Introduction: Genetic fuzzy systems." International Journal of Intelligent Systems 22, no. 9 (2007): 905–7. http://dx.doi.org/10.1002/int.20231.

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Herrera, Francisco, and Luis Magdalena. "Introduction: Genetic fuzzy systems." International Journal of Intelligent Systems 13, no. 10-11 (1998): 887–90. http://dx.doi.org/10.1002/(sici)1098-111x(199810/11)13:10/11<887::aid-int1>3.0.co;2-v.

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