Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Multi-Criteria Group Decision Making (MCGDM) aims to find a unique agreement from a number of decision makers/users by evaluating the uncertainty in judgments. In this paper, we present a General Type-2 Fuzzy Logic based approach for MCGDM (GFLMCGDM). The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers’ (DMs) judgments and the vagueness of the appraisal. In order to find the optimal parameters of the general type-2 fuzzy sets, we employed the Big Bang-Big Crunch (BB-BC) optimization. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs’ opinions into a unique approval. We present results from an application for the selection of reading lighting level in an intelligent environment. We carried out various experiments in the intelligent apartment (iSpace) located at the University of Essex. We found that the proposed GFL-MCGDM effectively handle the uncertainties between the various decision makers which resulted in producing outputs which better agreed with the users’ decision compared to type 1 and interval type 2 fuzzy based systems.
Wydawca
Rocznik
Tom
Strony
117--132
Opis fizyczny
Bibliogr. 36 poz., rys.
Bibliografia
- [1] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 110, 1986, 87 – 96.
- [2] R. M. Rodrıguez, L. Martınez, and F. Herrera, Hesitant Fuzzy Linguistic Term Sets for Decision Making, IEEE Transactions On Fuzzy Systems, 20, 1, 2012, 109-119.
- [3] R. R. Yager, Fuzzy decision making including unequal objectives, Fuzzy Sets and Systems, 1, 1978, 87-95, 1978.
- [4] L. A. Zadeh. Fuzzy sets, Information and Control, 8, 1865, 338–353.
- [5] S. D. Pohekar, Application of multi-criteria decision making to sustainable energy planning—A review,Renewable and Sustainable Energy Reviews, 8, 4, 2004, 365–381.
- [6] F. E.Borana, S. Gena, M. Kurtb, and M. Kurtb, A multi-criteria intuitionistic fuzzy group decisionmaking for supplier selection with TOPSIS method, Expert Systems with Applications, 36, 8, 2009, 11363–11368.
- [7] S. M. Chen, M. W. Yang, L. W. Lee, and S. W. Yang, Fuzzy multiple attributes group decisionmaking based on ranking interval type-2 fuzzy sets, Expert Systems with Applications, 39, 2012, 5295–5308.
- [8] S. M. Chen and L. W. Lee, Fuzzy multiple attributes group decision-making based on ranking values and the arithmetic operations of interval type-2 fuzzy sets, Expert Systems with Applications, 37, 1, 2012, 824–833.
- [9] S. M. Chen and L. W. Lee, Fuzzy multiple attributes group decision-making based on interval type-2 TOPSIS method, Expert Systems with Applications, 37, 4, 2012, 2790–2798.
- [10] S. M. Chen, and L. W. Lee, Fuzzy multiple criteria hierarchical group decision-making based on interval type-2 fuzzy sets, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, 40, 5, 2010, 1120-1128.
- [11] W. Wang, X. Liu, and Y. Qin, Multi-attribute group decision making models under interval type-2 fuzzy environment, Knowledge Based Systems, vol. 30, pp. 121-128, 2012.
- [12] C. Wagner, and H. Hagras, Novel Methods for the Design of General Type-2 fuzzy Sets based on Device Characteristics and Linguistic Labels Surveys, IFSA-EUSFLAT, ISBN: 978-989-95079-6-8, 2009, 537-543.
- [13] C. Wagner, and H. Hagras, Toward General Type-2 Fuzzy Logic Systems Based on zSlices, IEEE Transactions On Fuzzy Systems, 18, 4, 2012, 637-660.
- [14] O. Linda, and M. Manic, General Type-2 Fuzzy CMeans Algorithm for Uncertain Fuzzy Clustering, IEEE Transactions on Fuzzy Systems, 20, 5, 2012, 883-897.
- [15] S. Naim, and H. Hagras, A Fuzzy Logic Based Multi-Criteria Group Decision Making System for the Assessment of Umbilical Cord Acid-Base Balance, WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane, Australia, June, 10-15, 2012, 2122 – 2129.
- [16] S. Naim and H. Hagras, A Hybrid Approach for Multi-Criteria Group Decision Making Based on Interval Type-2 Fuzzy Logic and Intuitionistic Fuzzy Evaluation, WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane,Australia, June, 10-15, 2012, 1066 – 1073.
- [17] H. X. Zhang, F. Wang, and B. Zhang, Genetic optimization of fuzzy membership functions, Proceedings of the 2009 International Conference on Wavelet Analysis and Pattern Recognition, Baoding, 2009, 465-470.
- [18] T. Kumbasar, I. Eksin, M. Guzelkaya and E. Yesil, Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm, Expert Systems with Applications, 38, 2011, 12356–12364.
- [19] C. V. Camp and F. Huq, CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch algorithm, Original Research Article Engineering Structures, 48, 2013, 363-372.
- [20] H. Tang, J. Zhou, S. Xue and L.Xie, Big Bang- Big Crunch optimization for parameter estimation in structural systems, Original Research Article Mechanical Systems and Signal Processing, 24, 8, 2010, 2888-2897.
- [21] O. K. Erol and I. Eksin, A new optimization method: Big Bang–Big Crunch, Advances in Engineering Software, 37, 2006, 106–111.
- [22] L.Cervantes and O. Castillo, Genetic optimization of membership functions in modular fuzzy controllers for complex problems, Recent Advances on Hybrid Intelligent Systems Studies in Computational Intelligence, 451, 2013, 51-62.
- [23] N. R. Cazarez-Castro, L. T.Aguilar and O. Castillo, Fuzzy logic control with genetic membership function parameter optimization for the output regulation of a servomechanism with nonlinear backlash, Expert Systems with Applications, 37, 6, 2010, 4368–4378.
- [24] W. Chen, R. Zhu and Y.Wu, Membership functions optimization of fuzzy control based on genetic algorithms, International Refrigeration and Air Conditioning Conference, 1998, 207-211.
- [25] A. M. Acilar and A. Arslan, Optimization of multiple input-output fuzzy membership functions using clonal selection algorithm, Journal Expert Systems with Applications, 38 3, 2011, 1374-1381.
- [26] G. Fang, N. M. Kwok and Q. Ha, Automatic fuzzy membership function tuning using the particle swarm optimization, IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008, 324, 328.
- [27] E. Turanoglu, E. Ozceylan and M. S. Kiran, Particle swarm optimization and artificial bee colony approaches to optimize of single input-output fuzzy membership functions, Proceedings of the 41st International Conference on Computers & Industrial Engineering, 2011, 542-547.
- [28] S. Vaneshani and H. Jazayeri-Rad, Optimized fuzzy control by particle swarm optimization technique for control of CSTR,World Academy of Science, Engineering and Technology, 59, 2011, 686-691.
- [29] J. M. Mendel, and R. I. B. John, Type-2 fuzzy sets made simple, IEEE Transactions on Fuzzy System,10, 2, 2002, 117-127.
- [30] Q. Liang, N. N. Karnik, and J. M. Mendel, Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, 30, 3, 2000, 329-339.
- [31] H. Hagras, A hierarchical Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots, IEEE Transactions on Fuzzy Systems, 12,4, 2004, 524-539.
- [32] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Upper Saddle River, NJ: Prentice-Hall, 2001.
- [33] W. Wang, X. Liu, and Y. Qin, Multi-attribute group decision making models under interval type-2 fuzzy environment, Knowledge Based Systems, 30, 2012, 121-128.
- [34] Z. Xu, Intuitionistic preference relations and their application in group decision making, Information Sciences, 177, 2007, 2363–2379.
- [35] R. Stecher, Let there be light—at least enough for reading in libraries, Bull Medical Library Association, 33, 2, 1945, 220-230.
- [36] A. Bilgin, J. Dooley, L. Whittington, H. Hagras, M. Henson, C. Wagner, A. Malibari, A. Al-Ghamdi, M. Al-haddad & D. Al-Ghazzawi, Dynamic Profile-Selection for zSlices Based Type-2 Fuzzy Agents Controlling Multi-User Ambient Intelligent Environments, Proceedings of the 2012 IEEE International Conference on Fuzzy Systems, Brisbane, June 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-04157a4a-6fe9-4391-a63b-929d1bee9e65