42 results on '"Pedrycz, Witold"'
Search Results
2. Efficient mining product-based fuzzy association rules through central limit theorem.
- Author
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Zhang, Zhongjie, Pedrycz, Witold, and Huang, Jian
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MATHEMATICS theorems ,FUZZY control systems ,STATISTICAL sampling ,EVOLUTIONARY algorithms ,COMBINATORIAL optimization ,BIG data - Abstract
In this study, we propose a fast algorithm to form product-based fuzzy association rules from large quantitative dataset, which reduces data size and ensures the quality of the obtained results. A method is designed to transform mining of fuzzy association rules to the binary counterpart. It is shown that the final results are not affected by this transformation. Then, an efficient sampling method is developed, where a sample is taken to replace the original large dataset, so the size of the dataset is reduced and the cost of scanning is also decreased. Through the central limit theorem, the size of sample can be set reasonably, so the deviation of support of any fuzzy itemset caused by sampling is limited in a small range with a high probability. Through a series of experiments, we show the advantages of the approach both the speed of the proposed algorithm and its reliability. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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3. Using variable reduction strategy to accelerate evolutionary optimization.
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Wu, Guohua, Pedrycz, Witold, Suganthan, P.N., and Li, Haifeng
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MATHEMATICAL variables ,EVOLUTIONARY algorithms ,COMBINATORIAL optimization ,PROBLEM solving ,COMPUTATIONAL complexity - Abstract
In this study, we introduce a novel approach of variable reduction and integrate it into evolutionary algorithms in order to reduce the complexity of optimization problems. We develop reduction processes of variable reduction for derivative unconstrained optimization problems (DUOPs) and constrained optimization problems (COPs) with equality constraints and active inequality constraints. Variable reduction uses the problem domain knowledge implied when investigating optimal conditions existing in optimization problems. For DUOPs, equations involving derivatives are considered while for COPs, we discuss equations expressing the equality constraints. From the relationships formed in this way, we obtain relationships among the variables that have to be satisfied by optimal solutions. According to such relationships, we can utilize some variables (referred to as core variables) to express some other variables (referred to as reduced variables). We show that the essence of variable reduction is to produce a minimum collection of core variables and a maximum number of reduced variables based on a system of equations. We summarize some application-oriented situations of variable reduction and stress several important issues related to the further application and development of variable reduction. Essentially, variable reduction can reduce the number of variables and eliminate equality constraints, thus reducing the dimensionality of the solution space and improving the efficiency of evolutionary algorithms. The approach can be applied to unconstrained, constrained, continuous and discrete optimization problems only if there are explicit variable relationships to be satisfied in the optimal conditions. We test variable reduction on real-world and synthesized DUOPs and COPs. Experimental results and comparative studies point at the effectiveness of variable reduction. [ABSTRACT FROM AUTHOR]
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- 2017
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4. A space search optimization algorithm with accelerated convergence strategies.
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Huang, Wei, Oh, Sung-Kwun, Guo, Zhaolu, and Pedrycz, Witold
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COMBINATORIAL optimization ,EVOLUTIONARY algorithms ,SPACE research ,STOCHASTIC convergence ,EVOLUTIONARY computation - Abstract
Highlights: [•] We propose a space search evolutionary algorithm with accelerated convergence strategies. [•] The overall framework of the SSEA involves three main search mechanisms. [•] Local space search, global space search, and opposition-based search are handled. [•] Accuracy and speed of convergence are shown especially in case of high-dimensional continuous optimization problems. [Copyright &y& Elsevier]
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- 2013
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5. An Optimization of Allocation of Information Granularity in the Interpretation of Data Structures: Toward Granular Fuzzy Clustering.
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Pedrycz, Witold and Bargiela, Andrzej
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COMBINATORIAL optimization , *INFORMATION processing , *DATA structures , *FUZZY clustering technique , *COMPUTER algorithms , *GENERALIZATION , *FUZZY sets , *INDEXES - Abstract
Clustering forms one of the most visible conceptual and algorithmic framework of developing information granules. In spite of the algorithm being used, the representation of information granules–clusters is predominantly numeric (coming in the form of prototypes, partition matrices, dendrograms, etc.). In this paper, we consider a concept of granular prototypes that generalizes the numeric representation of the clusters and, in this way, helps capture more details about the data structure. By invoking the granulation–degranulation scheme, we design granular prototypes being reflective of the structure of data to a higher extent than the representation that is provided by their numeric counterparts (prototypes). The design is formulated as an optimization problem, which is guided by the coverage criterion, meaning that we maximize the number of data for which their granular realization includes the original data. The granularity of the prototypes themselves is treated as an important design asset; hence, its allocation to the individual prototypes is optimized so that the coverage criterion becomes maximized. With this regard, several schemes of optimal allocation of information granularity are investigated, where interval-valued prototypes are formed around the already produced numeric representatives. Experimental studies are provided in which the design of granular prototypes of interval format is discussed and characterized. [ABSTRACT FROM AUTHOR]
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- 2012
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6. Score-Based Resampling Method for Evolutionary Algorithms.
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Jonghwan Park, Moongu Jeon, and Pedrycz, Witold
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RESAMPLING (Statistics) ,GENETIC algorithms ,COMBINATORIAL optimization ,GENES ,GENETIC recombination ,CHROMOSOMES ,BEHAVIORAL assessment ,COMPLEXITY (Philosophy) ,MEDICAL function tests - Abstract
In this paper, a gene-handling method for evolutionary algorithms (EAs) is proposed. Such algorithms are characterized by a nonanalytic optimization process when dealing with complex systems as multiple behavioral responses occur in the realization of intelligent tasks. In generic EAs which optimize internal parameters of a given system, evaluation and selection are performed at the chromosome level. When a survived chromosome includes noneffective genes, the solution can be trapped in a local optimum during evolution, which causes an increase in the uncertainty of the results and reduces the quality of the overall system. This phenomenon also results in an unbalanced performance of partial behaviors. To alleviate this problem, a score-based resampling method is proposed, where a score function of a gene is introduced as a criterion of handling genes in each allele. The proposed method was empirically evaluated with various test functions, and the results show its effectiveness. [ABSTRACT FROM AUTHOR]
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- 2008
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7. Identification of fuzzy systems by means of genetic optimization and data granulation.
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Sung-Kwun Oh, Pedrycz, Witold, and Keon-Jun Park
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FUZZY systems , *GENETIC algorithms , *NONLINEAR systems , *COMBINATORIAL optimization , *MATHEMATICAL functions - Abstract
In this study, we introduce a new category of fuzzy inference systems based on data (information) granulation and show their applications to the identification of complex and usually nonlinear systems. Information granules are treated as collections of objects (data, in particular) brought together by the criteria of proximity, similarity, or functionality. The formal framework of information granulation along with the information granules themselves become an important design feature of fuzzy models, which in essence are geared towards capturing relationship between information granules rather than plain numeric data. The key characteristics of experimental data being used in the construction of the fuzzy model are carefully reflected by fuzzy rules formed therein. Information granulation realized with the aid of Hard C-Means (HCM) clustering helps determine the initial values of the parameters of the fuzzy models. This in particular concerns such important components of the rules as the initial apexes of the membership functions standing in the premise part of the fuzzy rules and the initial values of the polynomial functions present in their consequence part. The initial values of the parameters are tuned effectively with the aid of the genetic algorithms (GAs) and the least square method (LSM). An aggregate objective function is constructed in order to strike a sound balance between the approximation and generalization capabilities of the fuzzy model. The model is evaluated with the use of numerical experimentation and contrasted with the quality of some "conventional" fuzzy models already encountered in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2007
8. Genetically Optimized Fuzzy Polynomial Neural Networks.
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Sung-Kwun Oh, Pedrycz, Witold, and Ho-Sung Park
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ARTIFICIAL neural networks ,FUZZY algorithms ,ARTIFICIAL intelligence ,FUZZY mathematics ,GENETIC algorithms ,COMBINATORIAL optimization ,GMDH algorithms - Abstract
In this paper, we introduce a new topology of fuzzy polynomial neural networks (FPNNs) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs). The study offers a comprehensive design methodology involving mechanisms of genetic optimization, especially those exploiting genetic algorithms (GAs). Let us recall that the design of the ‘conventional’ FPNNs uses an extended group method of data handling (GMDH) and uses a fixed scheme of fuzzy inference (such as simplified, linear, and regression polynomial fuzzy inference) in each FPN of the network. It also considers a fixed number of input nodes (as being selected in advance by a network designer) at FPNs (or nodes) located in each layer. However such design process does not guarantee that the resulting FPNs will always result in an optimal networks architecture. Here, the development of the FPNN gives rise to a structurally optimized topology and comes with a substantial level of flexibility which becomes apparent when contrasted with the one we encounter in the conventional FPNNs. The design of each layer of the FPNN deals with its structural optimization involving a selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial forming a consequent part of fuzzy rules and a collection of the specific subset of input variables) and addresses detailed aspects of parametric optimization. Along this line, two general optimization mechanisms are explored. The structural optimization is realized via GAs. In case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network becomes generated in a dynamic fashion. To evaluate the performance of the genetically optimized FPNN (gFPNN), we experimented with two time series data (gas furnace and chaotic time series) as well as some synthetic data. A comparative analysis reveals that the proposed FPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature. [ABSTRACT FROM AUTHOR]
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- 2006
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9. Genetic design of feature spaces for pattern classifiers
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Pedrycz, Witold, Breuer, Arnon, and Pizzi, Nicolino J.
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PATTERN recognition systems , *GENETIC algorithms , *CURVE fitting , *COMBINATORIAL optimization - Abstract
Functional piecewise approximation seeks data representation that is compact, highly simplified and meaningful. This study presents a genetic algorithm (GA)-based approach for computing a piecewise polynomial representation of functions, with the focus being on piecewise linear approximation in an application of biomedical spectral data. The area of piecewise linear approximation has been researched in the past four decades approximately, and the method presented here is compared with another well-known approach. The expansion of this method to piecewise polynomial representation is shown to be straightforward. Finally, the application of this method as a feature extraction method for classification of a dataset of feature vectors, specifically biomedical spectra, is demonstrated. [Copyright &y& Elsevier]
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- 2004
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10. Multilayer Hybrid Fuzzy Neural Networks: Synthesis via Technologies of Advanced Computational Intelligence.
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Sung-Kwun Oh, Pedrycz, Witold, and Byoung-Jun Park
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ARTIFICIAL neural networks , *COMPUTATIONAL intelligence , *ARTIFICIAL intelligence , *FUZZY sets , *SET theory , *GENETIC algorithms , *COMBINATORIAL optimization - Abstract
In this paper, we develop an advanced architecture and come up with a comprehensive design methodology of genetically optimized hybrid fuzzy neural networks (gHFNNs). The construction of gHFNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNN results from a highly synergistic usage of the genetic optimization-driven hybrid system being generated by combining fuzzy neural networks (FNNs) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. The optimization of the FNN is realized with the aid of a standard backpropagation learning algorithm and genetic optimization. As the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method-based learning (optimization). Through the consecutive process of such structural and parametric optimization, an optimized PNN becomes generated in a dynamic fashion. To evaluate the performance of the gHFNNs, we experimented with a number of representative numerical examples. A comparative analysis demonstrates that the proposed gHFNNs are neurofuzzy systems with higher accuracy as well as more superb predictive capability than other models available in the literature. [ABSTRACT FROM AUTHOR]
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- 2006
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11. Benchmark Mobility Problems Using Real-World Data: The Example of Bus Stops Spacing Problem for the City of Calais
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Vendi, Valentin, Fonlupt, Cyril, Verel, Sébastien, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Benadada, Youssef, editor, Mhada, Fatima-Zahra, editor, Boukachour, Jaouad, editor, Ouzayd, Fatima, editor, and El Hilali Alaoui, Ahmed, editor
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- 2024
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12. Analysis of Various GNNs in Solving MaxCut Problem
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Hameed, Hiba, Ramanujan, Ajeesh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Gunjan, Vinit Kumar, editor, and Zurada, Jacek M., editor
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- 2024
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13. Performance Evaluation of Job Shop Scheduling Problem Using Proposed Hybrid of Black Hole and Firefly Algorithms
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Kaur, Jaspreet, Pal, Ashok, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Shrivastava, Vivek, editor, Tripathi, Ashish Kumar, editor, and Wang, Lipo, editor
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- 2024
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14. Integrated Forecast and Optimization for Retailer Allocation in a Two-Echelon Inventory System
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Maniezzo, Vittorio, Zhou, Tingting, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Herrero, Álvaro, editor, Calvo Rolle, José Luis, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor
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- 2023
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15. Extended Rank-Based Ant Colony Optimization Algorithm for Traveling Salesman Problem
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Pérez-Carabaza, Sara, Gálvez, Akemi, Iglesias, Andrés, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Herrero, Álvaro, editor, Calvo Rolle, José Luis, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor
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- 2023
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16. A Constructive Heuristic 'MDSA' Solving the Flexible Job Shop Scheduling Problem
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Guliashki, Vassil, Mušič, Gašper, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor
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- 2023
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17. Unfolding Computation Graph for Dynamic Planning Under Uncertainty
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Knyazeva, Margarita, Bozhenyuk, Alexander, Samoylov, Leontiy, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kahraman, Cengiz, editor, Sari, Irem Ucal, editor, Oztaysi, Basar, editor, Cebi, Selcuk, editor, Cevik Onar, Sezi, editor, and Tolga, A. Çağrı, editor
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- 2023
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18. Genetic Algorithm for a Non-standard Complex Problem of Partitioning and Vehicle Routing
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Erdei, Rudolf, Delinschi, Daniela, Matei, Oliviu, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, and Silhavy, Petr, editor
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- 2023
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19. Cat Swarm Optimization Algorithm for DNA Fragment Assembly Problem
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Yassine, Asmae, Bouzidi, Morad, Riffi, Mohammed Essaid, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ezziyyani, Mostafa, editor, and Balas, Valentina Emilia, editor
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- 2023
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20. A New Collective Simulated Annealing with Adapted Objective Function for Web Service Selection
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Bouhouche, Abdelouahid, Benmohammed, Mohamed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Laouar, Mohamed Ridda, editor, Balas, Valentina Emilia, editor, Lejdel, Brahim, editor, Eom, Sean, editor, and Boudia, Mohamed Amine, editor
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- 2023
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21. Icebreaker Fleet Management in Simulation Models of the Arctic Marine Transport Systems
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Topaj, Alex, Buyanov, Alexander, Karpenko, Alexander, Malykhanov, Andrey, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Guda, Alexander, editor
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- 2023
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22. A Deep Reinforcement Learning Algorithm Using A New Graph Transformer Model for Routing Problems
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Wang, Yang, Chen, Zhibin, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor
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- 2023
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23. On the Travelling Salesman Problem with Neighborhoods in a Polygonal World
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Kulich, Miroslav, Vidašič, Jan, Mikula, Jan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Cascalho, José M., editor, Tokhi, Mohammad Osman, editor, Silva, Manuel F., editor, Mendes, Armando, editor, Goher, Khaled, editor, and Funk, Matthias, editor
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- 2023
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24. A GRASP Algorithm for Multi-objective Airport Gate Assignment Problem
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Paldrak, Mert, Örnek, Mustafa Arslan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kahraman, Cengiz, editor, Tolga, A. Cagri, editor, Cevik Onar, Sezi, editor, Cebi, Selcuk, editor, Oztaysi, Basar, editor, and Sari, Irem Ucal, editor
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- 2022
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25. Modified Ant System with Threshold for the Vehicle Routing Problem
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Rungwachira, Petcharat, Thammano, Arit, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Meesad, Phayung, editor, Sodsee, Sunantha, editor, Jitsakul, Watchareewan, editor, and Tangwannawit, Sakchai, editor
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- 2022
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26. Solving a Centralized Dynamic Group Key Management Problem by an Optimization Approach
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Nguyen, Thi Tuyet Trinh, Luu, Hoang Phuc Hau, Le Thi, Hoai An, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Le Thi, Hoai An, editor, Pham Dinh, Tao, editor, and Le, Hoai Minh, editor
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- 2022
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27. Solving Travelling Salesman Problem Using a Modified Grey Wolf Optimizer
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Boualem, S. M., Meftah, B., Debbat, F., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Hatti, Mustapha, editor
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- 2022
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28. A Binary Whale Algorithm Applied to Knapsack Problem
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Moraga, Paola, Valenzuela, Pamela, Altimiras, Francisco, Lopez, Luis, Rubio, José-Miguel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor
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- 2021
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29. A Machine Learning Whale Algorithm Applied to the Resource Allocation Problems
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Jorquera, Lorena, Moraga, Paola, Altimiras, Francisco, Valenzuela, Pamela, Rubio, José-Miguel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor
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- 2021
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30. A New Genetic Improvement Operator Based on Frequency Analysis for Genetic Algorithms Applied to Job Shop Scheduling Problem
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Viana, Monique Simplicio, Contreras, Rodrigo Colnago, Junior, Orides Morandin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor
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- 2021
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31. Constrained Clustering Problems: NewOptimization Algorithms
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Ibn-Khedher, Hatem, Hadji, Makhlouf, Khedher, Mohamed Ibn, Khebbache, Selma, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor
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- 2021
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32. A Binary Firefly Algorithm Applied to Knapsack Problem
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Pinto, Hernan, Valenzuela, Matias, Maureira, Carlos, Lopez, Luis, Fernández, Andrés, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Silhavy, Radek, editor
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- 2021
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33. A Machine Learning Firefly Algorithm Applied to the Resource Allocation Problems
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Pinto, Hernan, Peña, Alvaro, Maureira, Carlos, Valenzuela, Matías, Villavicencio, Gabriel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Silhavy, Radek, editor
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- 2021
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34. Analysis of Impact of Possibilities of Modern Computers on Applicability of Combinatorial Optimization
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Isić, Safet, Obradović, Munib, Mehremić, Semir, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Karabegović, Isak, editor
- Published
- 2021
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35. Optimized face recognition algorithm using radial basis function neural networks and its practical applications.
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Yoo, Sung-Hoon, Oh, Sung-Kwun, and Pedrycz, Witold
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HUMAN facial recognition software , *COMPUTER algorithms , *COMBINATORIAL optimization , *RADIAL basis functions , *ARTIFICIAL neural networks , *HYBRID systems , *IMAGE quality in imaging systems - Abstract
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard ‘if-then’ format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. [ABSTRACT FROM AUTHOR]
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- 2015
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36. An Improved Local Search Genetic Algorithm with Multi-crossover for Job Shop Scheduling Problem
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Viana, Monique Simplicio, Junior, Orides Morandin, Contreras, Rodrigo Colnago, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor
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- 2020
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37. The design of a fuzzy cascade controller for ball and beam system: A study in optimization with the use of parallel genetic algorithms
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Oh, Sung-Kwun, Jang, Han-Jong, and Pedrycz, Witold
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GENETIC algorithms , *GENETIC programming , *COMBINATORIAL optimization , *MATHEMATICAL optimization - Abstract
Abstract: In this study, we introduce a design methodology for an optimized fuzzy cascade controller for ball and beam system by exploiting the use of hierarchical fair competition-based genetic algorithm (HFCGA). The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball and exhibits a number of interesting and challenging properties when considered from the control perspective. The position of ball is determined through the control of a servo motor. The displacement change of the position of ball requires the change of the angle of the beam which determines the position angle of a servo motor. Consequently, the variation of the position of the moving ball and the ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer (1st) controller and the inner (2nd) controller in a cascaded architecture. Auto-tuning of the parameters of the controller (viz. scaling factors) of each fuzzy controller is realized with the use of the HFCGA. The set-point value of the inner controller (the 2nd controller) corresponds to the position angle of a servo motor, and is given as a reference value which enters into the inner controller as the 2nd controller of the two cascaded controllers. HFCGA is a kind of a parallel genetic algorithm (PGA), which helps alleviate an effect of premature convergence being a potential shortcoming present in conventional genetic algorithms (GAs). A detailed comparative analysis carried out from the viewpoint of the performance and the design methodology, is provided for the fuzzy cascade controller and the conventional PD cascade controller whose design relied on the use of the serial genetic algorithms. [Copyright &y& Elsevier]
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- 2009
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38. IG-based genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons
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Oh, Sung-Kwun, Roh, Seok-Beom, Pedrycz, Witold, and Ahn, Tae-Chon
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ARTIFICIAL neural networks , *GEOMETRY , *STATISTICAL correlation , *COMBINATORIAL optimization , *GENETIC programming - Abstract
Abstract: In this study, we introduce and investigate a new topology of fuzzy-neural networks—fuzzy polynomial neural networks (FPNN) that is based on a genetically optimized multiplayer perceptron with fuzzy set-based polynomial neurons (FSPNs). We also develop a comprehensive design methodology involving mechanisms of genetic optimization and information granulation. In the sequel, the genetically optimized FPNN (gFPNN) is formed with the use of fuzzy set-based polynomial neurons (FSPNs) composed of fuzzy set-based rules through the process of information granulation. This granulation is realized with the aid of the C-means clustering (C-Means). The design procedure applied in the construction of each layer of an FPNN deals with its structural optimization involving the selection of the most suitable nodes (or FSPNs) with specific local characteristics (such as the number of input variable, the order of the polynomial, the number of membership functions, and a collection of specific subset of input variables) and address main aspects of parametric optimization. Along this line, two general optimization mechanisms are explored. The structural optimization is realized via genetic algorithms (GAs) and HCM method whereas in case of the parametric optimization we proceed with a standard least square estimation (learning). Through the consecutive process of structural and parametric optimization, a flexible neural network is generated in a dynamic fashion. The performance of the designed networks is quantified through experimentation where we use two modeling benchmarks already commonly utilized within the area of fuzzy or neurofuzzy modeling. [Copyright &y& Elsevier]
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- 2007
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39. Genetic algorithms for hardware–software partitioning and optimal resource allocation
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Purnaprajna, Madhura, Reformat, Marek, and Pedrycz, Witold
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GENETIC algorithms , *COMBINATORIAL optimization , *RESOURCE management , *SYSTEMS development - Abstract
Abstract: A scheme for time and power efficient embedded system design, using hardware and software components, is presented. Our objective is to reduce the execution time and the power consumed by the system, leading to the simultaneous multi-objective minimization of time and power. The goal of suitably partitioning the system into hardware and software components is achieved using Genetic Algorithms (GA). Multiple tests were conducted to confirm the consistency of the results obtained and the versatile nature of the objective functions. An enhanced resource constrained scheduling algorithm is used to determine the system performance. To emulate the characteristics of practical systems, the influence of inter-processor communication is examined. The suitability of introducing a reconfigurable hardware resource over pre-configured hardware is explored for the same objectives. The distinct difference in the task to resource mapping with the variation in design objective is studied. Further, the procedure to allocate optimal number of resources based on the design objective is proposed. The implementation is constrained for power and time individually, with GA being used to arrive at the resource count to suit the objective. The results obtained are compared by varying the time and power constraints. The test environment is developed using randomly generated task graphs. Exhaustive sets of tests are performed on the set design objectives to validate the proposed solution. [Copyright &y& Elsevier]
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- 2007
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40. Genetic learning of fuzzy cognitive maps
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Stach, Wojciech, Kurgan, Lukasz, Pedrycz, Witold, and Reformat, Marek
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GEOGRAPHICAL perception , *COMBINATORIAL optimization , *GENETIC algorithms , *GENETIC programming - Abstract
Abstract: Fuzzy cognitive maps (FCMs) are a very convenient, simple, and powerful tool for simulation and analysis of dynamic systems. They were originally developed in 1980 by Kosko, and since then successfully applied to numerous domains, such as engineering, medicine, control, and political affairs. Their popularity stems from simplicity and transparency of the underlying model. At the same time FCMs are hindered by necessity of involving domain experts to develop the model. Since human experts are subjective and can handle only relatively simple networks (maps), there is an urgent need to develop methods for automated generation of FCM models. This study proposes a novel learning method that is able to generate FCM models from input historical data, and without human intervention. The proposed method is based on genetic algorithms, and requires only a single state vector sequence as an input. The paper proposes and experimentally compares several different design alternatives of genetic optimization and thoroughly tests and discusses the best design. Extensive benchmarking tests, which involve 200 FCMs with varying size and density of connections, performed on both synthetic and real-life data quantifies the performance of the development method and emphasizes its suitability. [Copyright &y& Elsevier]
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- 2005
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41. Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation
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Choi, Jeoung-Nae, Oh, Sung-Kwun, and Pedrycz, Witold
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ALGORITHMS , *COMBINATORIAL optimization , *GENETIC algorithms , *MATHEMATICAL optimization , *GRANULATION , *METHODOLOGY - Abstract
Abstract: In this paper, we develop a design methodology for information granulation-based genetically optimized fuzzy inference system, which deals with the tuning method with a variant identification ratio for structural as well as parametric optimization of the reasoning system. The tuning is carried out with the aid of the hierarchical fair competition-based parallel genetic algorithms and it employs the mechanism of information granulation. This version of the genetic algorithm is a multi-population variant of parallel genetic algorithms, which is particularly suitable for handling multimodal problems of high-dimensionality. The granulation of information is realized with the aid of the C-Means clustering algorithm. The concept of information granulation is applied to the formation of the fuzzy inference system in order to realize its structural optimization. Here we divide the input space in order to construct the premise part of the fuzzy rules. Subsequently the consequence part of each fuzzy rule is organized based on the center points (prototypes) of data group obtained as a result of clustering. In particular, this concerns the fuzzy inference system-related parameters, i.e., the number of input variables to be used in the fuzzy inference system, a collection of a specific subset of input variables, the number of membership functions used for each input variable, and the polynomial type (order) occurring at the consequence part of fuzzy rules. Making use of a mechanism of simultaneous tuning for the parameters, we construct an optimized fuzzy inference system related to its structural as well as parametric optimization. A comparative analysis demonstrates that the proposed methodology leads to improved results when compared with some conventional methods exploited in fuzzy modeling. [Copyright &y& Elsevier]
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- 2008
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42. Socially aware fuzzy vehicle routing problem: A topic modeling based approach for driver well-being.
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Khaitan, Anisha, Kumar Mehlawat, Mukesh, Gupta, Pankaj, and Pedrycz, Witold
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VEHICLE routing problem , *NATURAL language processing , *QUANTITATIVE research , *FATIGUE (Physiology) , *COMBINATORIAL optimization , *GENETIC algorithms - Abstract
• Driver well-being is important to prevent accidents, aggression and absenteeism. • We use topic modeling to extract central ideas on driver fatigue, stress, and burnout. • Drivers need more breaks to improve health, control aggression and prevent accidents. • An integrated model is created to improve driver well-being and minimize travel time. • A hybrid genetic algorithm is used to solve the ensuing fuzzy vehicle routing problem. Drivers are essential to any industry offering transportation and logistics services. Ensuring their well-being ensures smooth business and reduces stress factors that cause driver burnout and increase fatigue and stress. Burnout is usually responsible for accidents, absenteeism, and other similar problems, that are best prevented. Therefore, understanding these stress factors and determining ways to overcome them would benefit any related industry. In the proposed approach, we leverage the benefits of natural language processing and the availability of numerous studies on driver burnout, fatigue, and stress to determine the various stress factors and to understand how to address those using a vehicle routing problem. First, topic modeling, a popular natural language processing technique, is used to extract the different topics of discussion around driver burnout, stress, and fatigue. Next, the extracted cases are qualitatively analyzed to ascertain the stress factors that can be controlled through a routing problem and how to do so. Since uncertainty is prevalent in real life, pairwise travel times are assumed to follow different functional forms. Finally, an integrated routing model is developed, and a hybrid genetic algorithm is coded to solve the model. The use of various sources and types of data, viz., structured data for routing and unstructured data for topic modeling, to obtain solutions for routing and driver well-being, simultaneously, is a notable contribution of the proposed approach. Also, the integrated use of qualitative and quantitative research methods and the combination of text analytics and combinatorial optimization to model social problems is a relatively new concept in the vehicle routing literature. Experimental studies on existing datasets provide solutions that illustrate the advantages of the approach. Insights are also provided to assist managers in decision-making under similar scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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