Advances in Fuzzy Systems The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Fuzzy Rules for Ant Based Clustering Algorithm Thu, 27 Oct 2016 15:58:24 +0000 This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS) algorithm with the fuzzy -means (FCM) clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy -means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of -means, -medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index. Amira Hamdi, Nicolas Monmarché, Mohamed Slimane, and Adel M. Alimi Copyright © 2016 Amira Hamdi et al. All rights reserved. Robust FCM Algorithm with Local and Gray Information for Image Segmentation Thu, 20 Oct 2016 09:33:41 +0000 The FCM (fuzzy -mean) algorithm has been extended and modified in many ways in order to solve the image segmentation problem. However, almost all the extensions require the adjustment of at least one parameter that depends on the image itself. To overcome this problem and provide a robust fuzzy clustering algorithm that is fully free of the empirical parameters and noise type-independent, we propose a new factor that includes the local spatial and the gray level information. Actually, this work provides three extensions of the FCM algorithm that proved their efficiency on synthetic and real images. Hanane Barrah, Abdeljabbar Cherkaoui, and Driss Sarsri Copyright © 2016 Hanane Barrah et al. All rights reserved. Fuzzy Constrained Probabilistic Inventory Models Depending on Trapezoidal Fuzzy Numbers Thu, 22 Sep 2016 07:35:08 +0000 We discussed two different cases of the probabilistic continuous review mixture shortage inventory model with varying and constrained expected order cost, when the lead time demand follows some different continuous distributions. The first case is when the total cost components are considered to be crisp values, and the other case is when the costs are considered as trapezoidal fuzzy number. Also, some special cases are deduced. To investigate the proposed model in the crisp case and the fuzzy case, illustrative numerical example is added. From the numerical results we will conclude that Uniform distribution is the best distribution to get the exact solutions, and the exact solutions for fuzzy models are considered more practical and close to the reality of life and get minimum expected total cost less than the crisp models. Mona F. El-Wakeel and Kholood O. Al-yazidi Copyright © 2016 Mona F. El-Wakeel and Kholood O. Al-yazidi. All rights reserved. Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data Wed, 14 Sep 2016 08:42:55 +0000 Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future. Munish Saini, Sandeep Mehmi, and Kuljit Kaur Chahal Copyright © 2016 Munish Saini et al. All rights reserved. Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor Sun, 04 Sep 2016 07:56:12 +0000 Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking, and classification via energy minimization. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO). However, conventional PSO converges slowly and gets trapped in local minimum easily which results in inaccurate detection of concavities in the object boundary. This is taken care of by using proposed multiswarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously through information sharing among them. The performance of the multiswarm PSO-based search process is further enhanced by using dynamic adaptation of the inertia factor. In this paper, we propose using a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches. Ajay Khunteta and D. Ghosh Copyright © 2016 Ajay Khunteta and D. Ghosh. All rights reserved. Fuzzy Pheromone Potential Fields for Virtual Pedestrian Simulation Sun, 21 Aug 2016 07:02:42 +0000 The study of collective movement of pedestrians is crucial in various situations, such as evacuation of buildings, stadiums, or external events like concerts or public events. In such situations and under panic conditions, several incidents and disasters may arise, resulting in loss of human lives. Hence, the study and modeling of the pedestrians behavior are imperative in both normal and panic situations. In a previous work, we developed a microscopic model for pedestrian movement based on the algorithm of Ant Colonies and the principles of cellular automata. We took advantage of a fuzzy model to better reflect the uncertainty and vagueness of the perception of space to pedestrians, especially to represent the desirability or blurred visibility of virtual pedestrians. This paper uses the mechanism of artificial potential fields. Said fields provide virtual pedestrians with better visibility of their surroundings and its various components (goals and obstacles). The predictions provided by the first-order traffic flow theory are confirmed by the results of the simulation. The advantage of this model lies in the combination of benefits provided by the model of ants and artificial potential fields in a fuzzy modeling, to better understand the perceptions of pedestrians. Meriem Mandar and Azedine Boulmakoul Copyright © 2016 Meriem Mandar and Azedine Boulmakoul. All rights reserved. A Similarity Classifier with Bonferroni Mean Operators Tue, 26 Jul 2016 10:03:46 +0000 A similarity classifier based on Bonferroni mean based operators is introduced. The new Bonferroni mean based variant of the similarity classifier is also extended to cover a new Bonferroni-OWA variant. The new Bonferroni-OWA based similarity classifier raises the question of how to accomplish the weighting needed and for this reason we also examine a number of linguistic quantifiers for weight generation. The new proposed similarity classifier variants are tested on four real world medical research related data sets. The results are compared with results from two previously presented similarity classifiers, one based on the generalized mean and another based on an arithmetic mean operator. The results show that comparatively better classification accuracy can be reached with the proposed new similarity classifier variants. Onesfole Kurama, Pasi Luukka, and Mikael Collan Copyright © 2016 Onesfole Kurama et al. All rights reserved. An Improved Fuzzy Based Missing Value Estimation in DNA Microarray Validated by Gene Ranking Mon, 18 Jul 2016 14:17:37 +0000 Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This work starts with a microarray dataset containing multiple missing values. We first apply the modified version of the fuzzy theory based existing method LRFDVImpute to impute multiple missing values of time series gene expression data and then validate the result of imputation by genetic algorithm (GA) based gene ranking methodology along with some regular statistical validation techniques, like RMSE method. Gene ranking, as far as our knowledge, has not been used yet to validate the result of missing value estimation. Firstly, the proposed method has been tested on the very popular Spellman dataset and results show that error margins have been drastically reduced compared to some previous works, which indirectly validates the statistical significance of the proposed method. Then it has been applied on four other 2-class benchmark datasets, like Colorectal Cancer tumours dataset (GDS4382), Breast Cancer dataset (GSE349-350), Prostate Cancer dataset, and DLBCL-FL (Leukaemia) for both missing value estimation and ranking the genes, and the results show that the proposed method can reach 100% classification accuracy with very few dominant genes, which indirectly validates the biological significance of the proposed method. Sujay Saha, Anupam Ghosh, Dibyendu Bikash Seal, and Kashi Nath Dey Copyright © 2016 Sujay Saha et al. All rights reserved. An Exhaustive Study of Possibility Measures of Interval-Valued Intuitionistic Fuzzy Sets and Application to Multicriteria Decision Making Tue, 12 Jul 2016 07:08:20 +0000 This work is interested in showing the importance of possibility theory in multicriteria decision making (MCDM). Thus, we apply some possibility measures from literature to the MCDM method using interval-valued intuitionistic fuzzy sets (IVIFSs). These measures are applied to a decision matrix after being transformed with aggregation operators. The results are compared between each other and concluding remarks are drawn. Fatma Dammak, Leila Baccour, and Adel M. Alimi Copyright © 2016 Fatma Dammak et al. All rights reserved. A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples Thu, 23 Jun 2016 13:32:41 +0000 Although the goal of clustering is to reveal structural information from unlabeled datasets, in cases with partial structural supervisions, semi-supervised clustering is expected to improve partition quality. However, in many real applications, it may cause additional costs to provide an enough amount of supervised objects with class labels. A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated from supervised objects. In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering, where the goal is to reveal object-item pairwise cluster structures from cooccurrence information among them. Several experimental results demonstrate the characteristics of the proposed approach. Daiji Tanaka, Katsuhiro Honda, Seiki Ubukata, and Akira Notsu Copyright © 2016 Daiji Tanaka et al. All rights reserved. On the Existence and Uniqueness for High Order Fuzzy Fractional Differential Equations with Uncertainty Wed, 08 Jun 2016 11:25:43 +0000 A class fuzzy fractional differential equation (FFDE) involving Riemann-Liouville -differentiability of arbitrary order is considered. Using Krasnoselskii-Krein type conditions, Kooi type conditions, and Rogers conditions we establish the uniqueness and existence of the solution after determining the equivalent integral form of the solution. Abdourazek Souahi, Assia Guezane-Lakoud, and Amara Hitta Copyright © 2016 Abdourazek Souahi et al. All rights reserved. Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique Wed, 08 Jun 2016 06:17:56 +0000 The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES) is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties. Ismail Hossain, Imtiaz Ahmed Choudhury, Azuddin Bin Mamat, Abdus Shahid, Ayub Nabi Khan, and Altab Hossain Copyright © 2016 Ismail Hossain et al. All rights reserved. An Efficient Ranking Technique for Intuitionistic Fuzzy Numbers with Its Application in Chance Constrained Bilevel Programming Thu, 28 Apr 2016 11:40:35 +0000 The aim of this paper is to develop a new ranking technique for intuitionistic fuzzy numbers using the method of defuzzification based on probability density function of the corresponding membership function, as well as the complement of nonmembership function. Using the proposed ranking technique a methodology for solving linear bilevel fuzzy stochastic programming problem involving normal intuitionistic fuzzy numbers is developed. In the solution process each objective is solved independently to set the individual goal value of the objectives of the decision makers and thereby constructing fuzzy membership goal of the objectives of each decision maker. Finally, a fuzzy goal programming approach is considered to achieve the highest membership degree to the extent possible of each of the membership goals of the decision makers in the decision making context. Illustrative numerical examples are provided to demonstrate the applicability of the proposed methodology and the achieved results are compared with existing techniques. Animesh Biswas and Arnab Kumar De Copyright © 2016 Animesh Biswas and Arnab Kumar De. All rights reserved. The Lattice Structure of L-Contact Relations Mon, 18 Apr 2016 12:11:30 +0000 From the point of view of graded truth approach, we define the notion of a contact relation on the collection of all -sets, discuss the connection to the set of all close, reflexive, and symmetric relations on all -ultrafilters on , and investigate the algebraic structure of all -contact relations. Xueyou Chen Copyright © 2016 Xueyou Chen. All rights reserved. Designing of Vague Logic Based 2-Layered Framework for CPU Scheduler Wed, 13 Apr 2016 09:28:29 +0000 Fuzzy based CPU scheduler has become of great interest by operating system because of its ability to handle imprecise information associated with task. This paper introduces an extension to the fuzzy based round robin scheduler to a Vague Logic Based Round Robin (VBRR) scheduler. VBRR scheduler works on 2-layered framework. At the first layer, scheduler has a vague inference system which has the ability to handle the impreciseness of task using vague logic. At the second layer, Vague Logic Based Round Robin (VBRR) scheduling algorithm works to schedule the tasks. VBRR scheduler has the learning capability based on which scheduler adapts intelligently an optimum length for time quantum. An optimum time quantum reduces the overhead on scheduler by reducing the unnecessary context switches which lead to improve the overall performance of system. The work is simulated using MATLAB and compared with the conventional round robin scheduler and the other two fuzzy based approaches to CPU scheduler. Given simulation analysis and results prove the effectiveness and efficiency of VBRR scheduler. Supriya Raheja Copyright © 2016 Supriya Raheja. All rights reserved. Power Frequency Oscillation Suppression Using Two-Stage Optimized Fuzzy Logic Controller for Multigeneration System Tue, 12 Apr 2016 11:04:28 +0000 This paper attempts to develop a linearized model of automatic generation control (AGC) for an interconnected two-area reheat type thermal power system in deregulated environment. A comparison between genetic algorithm optimized PID controller (GA-PID), particle swarm optimized PID controller (PSO-PID), and proposed two-stage based PSO optimized fuzzy logic controller (TSO-FLC) is presented. The proposed fuzzy based controller is optimized at two stages: one is rule base optimization and other is scaling factor and gain factor optimization. This shows the best dynamic response following a step load change with different cases of bilateral contracts in deregulated environment. In addition, performance of proposed TSO-FLC is also examined for changes in system parameters with different type of contractual demands between control areas and compared with GA-PID and PSO-PID. MATLAB/Simulink® is used for all simulations. Y. K. Bhateshvar and H. D. Mathur Copyright © 2016 Y. K. Bhateshvar and H. D. Mathur. All rights reserved. Predicting Geotechnical Investigation Using the Knowledge Based System Tue, 05 Apr 2016 13:02:35 +0000 The purpose of this paper is to evaluate the optimal number of investigation points and each field test and laboratory test for a proper description of a building site. These optimal numbers are defined based on their minimum and maximum number and with the equivalent investigation ratio. The total increments of minimum and maximum number of investigation points for different building site conditions were determined. To facilitate the decision-making process for a number of investigation points, an Adaptive Network Fuzzy Inference System (ANFIS) was proposed. The obtained fuzzy inference system considers the influence of several entry parameters and computes the equivalent investigation ratio. The developed model (ANFIS-SI) can be applied to characterize any building site. The ANFIS-SI model takes into account project factors which are evaluated with a rating from 1 to 10. The model ANFIS-SI, with integrated recommendations can be used as a systematic decision support tool for engineers to evaluate the number of investigation points, field tests, and laboratory tests for a proper description of a building site. The determination of the optimal number of investigative points and the optimal number of each field test and laboratory test is presented on reference case. Bojan Žlender and Primož Jelušič Copyright © 2016 Bojan Žlender and Primož Jelušič. All rights reserved. Fuzzy Human Reliability Analysis: Applications and Contributions Review Mon, 04 Apr 2016 13:34:29 +0000 The applications and contributions of fuzzy set theory to human reliability analysis (HRA) are reassessed. The main contribution of fuzzy mathematics relies on its ability to represent vague information. Many HRA authors have made contributions developing new models, introducing fuzzy quantification methodologies. Conversely, others have drawn on fuzzy techniques or methodologies for quantifying already existing models. Fuzzy contributions improve HRA in five main aspects: (1) uncertainty treatment, (2) expert judgment data treatment, (3) fuzzy fault trees, (4) performance shaping factors, and (5) human behaviour model. Finally, recent fuzzy applications and new trends in fuzzy HRA are herein discussed. P. A. Baziuk, S. S. Rivera, and J. Núñez Mc Leod Copyright © 2016 P. A. Baziuk et al. All rights reserved. A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters Mon, 28 Mar 2016 06:35:14 +0000 Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy. Boominathan Perumal and Aramudhan Murugaiyan Copyright © 2016 Boominathan Perumal and Aramudhan Murugaiyan. All rights reserved. FCM Clustering Algorithms for Segmentation of Brain MR Images Tue, 15 Mar 2016 12:38:55 +0000 The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy -means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed. Yogita K. Dubey and Milind M. Mushrif Copyright © 2016 Yogita K. Dubey and Milind M. Mushrif. All rights reserved. Type-2 Fuzzy Logic Controller of a Doubly Fed Induction Machine Tue, 15 Mar 2016 08:36:52 +0000 Interval type-2 fuzzy logic controller (IT2FLC) method for controlling the speed with a direct stator flux orientation control of doubly fed induction motor (DFIM) is proposed. The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases it is proved that their robustness and performance are less sensitive to parameters variation over conventional controllers. The synthesis of stabilizing control laws design based on IT2FLC is developed. A comparative analysis between type-1 fuzzy logic controller (T1FLC) and IT2FLC of the DFIM is shown. Simulation results show the feasibility and the effectiveness of the suggested method to the control of the DFIM under different operating conditions such as load torque and in the presence of parameters variation. Keltoum Loukal and Leila Benalia Copyright © 2016 Keltoum Loukal and Leila Benalia. All rights reserved. Two-Stage Stratified Randomized Response Model with Fuzzy Numbers Thu, 10 Mar 2016 08:40:05 +0000 We consider an allocation problem in two-stage stratified Warner’s randomized response model and minimize the variance subject to cost constraint. The costs (measurement costs and total budget of the survey) in the cost constraint are assumed as fuzzy numbers, in particular triangular and trapezoidal fuzzy numbers due to the ease of use. The problem formulated is solved by using Lagrange multipliers technique and the optimum allocation obtained in the form of fuzzy numbers is converted into crisp form using -cut method at a prescribed value of . An illustrative numerical example is presented to demonstrate the proposed problem. Mohammad Faisal Khan, Neha Gupta, and Irfan Ali Copyright © 2016 Mohammad Faisal Khan et al. All rights reserved. An ELECTRE Approach for Multicriteria Interval-Valued Intuitionistic Trapezoidal Fuzzy Group Decision Making Problems Wed, 09 Mar 2016 13:48:56 +0000 The Multiple Criteria Decision Making (MCDM) is acknowledged as the most useful branch of decision making. It provides an effective framework for comparison based on the evaluation of multiple conflicting criteria. In this paper, a method is proposed to work out multiple attribute group decision making (MAGDM) problems with interval-valued intuitionistic trapezoidal fuzzy numbers (IVITFNs) using Elimination and Choice Translation Reality (ELECTRE) method. A new ranking function based on value and ambiguity is introduced to compare the IVITFNs, which overcomes the limitations of existing methods. An illustrative numerical example is solved to verify the efficiency of the proposed method to select the better alternative. Sireesha Veeramachaneni and Himabindu Kandikonda Copyright © 2016 Sireesha Veeramachaneni and Himabindu Kandikonda. All rights reserved. A New Approach for Solving Fully Fuzzy Linear Programming by Using the Lexicography Method Wed, 02 Mar 2016 12:51:55 +0000 The fully fuzzy linear programming (FFLP) problem has many different applications in sciences and engineering, and various methods have been proposed for solving this problem. Recently, some scholars presented two new methods to solve FFLP. In this paper, by considering the fuzzy numbers and the lexicography method in conjunction with crisp linear programming, we design a new model for solving FFLP. The proposed scheme presented promising results from the aspects of performance and computing efficiency. Moreover, comparison between the new model and two mentioned methods for the studied problem shows a remarkable agreement and reveals that the new model is more reliable in the point of view of optimality. A. Hosseinzadeh and S. A. Edalatpanah Copyright © 2016 A. Hosseinzadeh and S. A. Edalatpanah. All rights reserved. Research on Fuzzy Control Based Flexible Composite Winding System Mon, 15 Feb 2016 14:10:50 +0000 In the process of composite resin prepreg tape winding, the presence of pores or voids among the layers of composite can result in reduced strength of winding. To alleviate this problem, it is required that the composite tape winding machines be designed such that the layers of composite are evenly wound on the previous one. The paper presents a novel design of flexible winding system for composite tape winding. Based on the analysis of errors in winding process, the novel winding system eliminates winding point error and winding angle error based on the speed controlled flexible roller. This paper also presents the kinetic analysis of the novel system and its controller design. Experiments are conducted on the novel winding system. The experimental results illustrate that the novel flexible winding system has a good performance in winding accuracy. He Xiaodong, Shi Yaoyao, and Kang Chao Copyright © 2016 He Xiaodong et al. All rights reserved. A Modified FNN Fault Diagnosis on PCVD Microwave System Wed, 28 Oct 2015 07:44:22 +0000 A modified FNN fault diagnosis algorithm is presented in this paper for microwave subsystem of Plasma Chemical Vapor Deposition (PCVD). The symptom variables are selected as the crisp inputs, and the corresponding membership functions are obtained from premeasured data as well as experts’ diagnostic experience/knowledge. The prior probability and the restriction coefficients are combined into the FNN algorithm via matrix operator. This modified FNN algorithm is verified for PCVD fault diagnosis application and realizes the MIMO for multifault mode diagnosis. Zhenyu Li and Hongsheng Li Copyright © 2015 Zhenyu Li and Hongsheng Li. All rights reserved. Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO Tue, 27 Oct 2015 08:38:11 +0000 This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions. Abba Suganda Girsang, Chun-Wei Tsai, and Chu-Sing Yang Copyright © 2015 Abba Suganda Girsang et al. All rights reserved. A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System Mon, 28 Sep 2015 12:16:43 +0000 This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC. S. M. Odeh, A. M. Mora, M. N. Moreno, and J. J. Merelo Copyright © 2015 S. M. Odeh et al. All rights reserved. Quantitative Analyses and Development of a -Incrementation Algorithm for FCM with Tsallis Entropy Maximization Wed, 19 Aug 2015 09:03:05 +0000 Tsallis entropy is a -parameter extension of Shannon entropy. By extremizing the Tsallis entropy within the framework of fuzzy -means clustering (FCM), a membership function similar to the statistical mechanical distribution function is obtained. The Tsallis entropy-based DA-FCM algorithm was developed by combining it with the deterministic annealing (DA) method. One of the challenges of this method is to determine an appropriate initial annealing temperature and a value, according to the data distribution. This is complex, because the membership function changes its shape by decreasing the temperature or by increasing . Quantitative relationships between the temperature and are examined, and the results show that, in order to change equally, inverse changes must be made to the temperature and . Accordingly, in this paper, we propose and investigate two kinds of combinatorial methods for -incrementation and the reduction of temperature for use in the Tsallis entropy-based FCM. In the proposed methods, is defined as a function of the temperature. Experiments are performed using Fisher’s iris dataset, and the proposed methods are confirmed to determine an appropriate value in many cases. Makoto Yasuda Copyright © 2015 Makoto Yasuda. All rights reserved. Application of Fuzzy Optimization to the Orienteering Problem Mon, 13 Jul 2015 07:40:46 +0000 This paper deals with the orienteering problem (OP) which is a combination of two well-known problems (i.e., travelling salesman problem and the knapsack problem). OP is an NP-hard problem and is useful in appropriately modeling several challenging applications. As the parameters involved in these applications cannot be measured precisely, depicting them using crisp numbers is unrealistic. Further, the decision maker may be satisfied with graded satisfaction levels of solutions, which cannot be formulated using a crisp program. To deal with the above-stated two issues, we formulate the fuzzy orienteering problem (FOP) and provide a method to solve it. Here we state the two necessary conditions of OP of maximizing the total collected score and minimizing the time taken to traverse a path (within the specified time bound) as fuzzy goals and the remaining necessary conditions as crisp constraints. Using the max-min formulation of the fuzzy sets obtained from the fuzzy goals, we calculate the fuzzy decision sets ( and ) that contain the feasible paths and the desirable paths, respectively, along with the degrees to which they are acceptable. To efficiently solve large instances of FOP, we also present a parallel algorithm on CREW PRAM model. Madhushi Verma and K. K. Shukla Copyright © 2015 Madhushi Verma and K. K. Shukla. All rights reserved.