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This paper systematically constructs a multi-project and a multi-objective human resource scheduling mathematical model in construction projects and puts forward an improved genetic algorithm to solve it by aiming at the problems existing in the process of human resource scheduling and optimization. Specifically, first, the basic mathematical models of GPRs and resource-constrained construction project human resource scheduling problem (RCWSP/GPRs) are established, and the multi-project equilibrium problem is extended. Then, an improved inter-cluster separation (ICS) algorithm is proposed and used to solve the RCWSP/GPRs problem. Finally, on this basis, the mathematical model of multi-project and multi-objective human resource scheduling problem and the solution method based on multi-objective-integrated circuit are proposed. At the same time, the resource-constrained multi-project and multi-skill human resource scheduling problem and the integer programming mathematical model under the generalized priority relationship are proposed. Also, the simulation results verify the accuracy of the proposed algorithm and model. [ABSTRACT FROM AUTHOR]

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds' classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds' sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles' speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles' merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems. [ABSTRACT FROM AUTHOR]

In decision making with interval-valued intuitionistic fuzzy preference relations (IVIFPRs), the consistency and its improvement are key issues. The aim of this paper is to investigate a new multiplicative consistency of IVIFPRs based on interval arithmetic, and propose goal programming approach to improve consistency and consensus. Based on analysis of the drawbacks of the existing consistency definitions of IVIFPR, new definition for multiplicative consistent IVIFPR is introduced. Based on the proposed multiplicative consistency and analysis of the relationship between interval-valued intuitionistic fuzzy weights and multiplicative consistent IVIFPR, goal programming-based model is developed for deriving interval-valued intuitionistic fuzzy weights from IVIFPR. To measure the multiplicative consistency degree of IVIFPRs, a consistency index is introduced and then an acceptable consistency is defined. For an IVIFPR with unacceptable consistency, a mathematical programming approach is proposed to improve its consistency. By considering the experts' subjective and consensus weights, the experts' comprehensive weights are determined. Some numerical examples illustrate the proposed models. [ABSTRACT FROM AUTHOR]

Wavelets are a relatively recent development in applied mathematics. Motivated by the recent paper [On accumulated spectrograms, Trans. Amer. Math. Soc. 368 (2016) 3629-3649] by Abreu, Gröchenig and Romero, we will prove in the present paper related results in the context of wavelets and time-scale analysis. [ABSTRACT FROM AUTHOR]

Image stitching among images that have significant illumination changes will lead to unnatural mosaic image. An image stitching algorithm based on histogram matching and scale-invariant feature transform (SIFT) algorithm is brought out to solve the problem in this paper. First, histogram matching is used for image adjustment, so that the images to be stitched are at the same level of illumination, then the paper adopts SIFT algorithm to extract the key points of the images and performs the rough matching process, followed by RANSAC algorithm for fine matches, and finally calculates the appropriate mathematical mapping model between two images and according to the mapping relationship, a simple weighted average algorithm is used for image blending. The experimental results show that the algorithm is effective. [ABSTRACT FROM AUTHOR]

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes' opinions have not been considered. In this paper, a modified version of influence maximization which is named as "opinion-aware influence maximization" (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of optimum design of a foundation for rotating machinery with a view to minimize vibration transmission from a machine source to its supporting structure. The problem of analysis and optimization of the installation systems of machinery has been extensively researched on the assumption of a rigid supporting structure. The design based on a rigid supporting structure model is reasonable for the installation of machinery in many real engineering situations. However, this rigid support based model may not be appropriate for the problem studied herein where the machinery is to be installed on a relatively flexible supporting plate. Thus, a generalized mathematical model of mobility power flow is developed in this paper, with a rotating machine as vibration source, resilient mounts as isolator, and a flexible sup-porting plate as receiver. The objective of minimizing vibration transmission is realized by optimization of stiffness coefficients of resilient mounts with a constraint on the vibration level of the machine. The design objective is chosen as the minimization of the power flow transmitted to the flexible supporting plate through the resilient mounts at the excitation frequency of the machinery. Both a single excitation frequency and a range of excitation frequencies are considered. A gradient-based mathematical programing method is selected for its advantage of efficiently solving the current type of optimization problem with multiple mounts and multi-degree-of-freedom vibration transmission. The sensitivities of the objective and the constraint functions with respect to the design variables are derived analytically. The design and per-formance of the optimized machinery foundation is illustrated and discussed using several numerical examples. The optimized mounting system is suitable for the installation case of a rotating machine with a low or medium service speed on a flexible supporting plate structure. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to generate ensemble classifier by joint optimization of accuracy and diversity. It is expected that the base classifiers in an ensemble are accurate and diverse (i.e., complementary iii terms of errors) among each other for the ensemble classifier to be more accurate. We adopt a multi-objective evolutionary algorithm (MOEA) for joint optimization of accuracy and diversity on our recently developed nonuniform layered cluster oriented ensemble classifier (NULCOEC). In NULCOEC, the data set is partitioned into a variable number of clusters at different layers. Base classifiers are then trained on the clusters at different layers. The performance of NULCOEC is a function of the vector of the number of layers and clusters. The research presented in this paper investigates the implication of applying MOEA to generate NULCOEC. Accuracy and diversity of the ensemble classifier is expressed as a function of layers and clusters. A MOEA then searches for the combination of layers and clusters to obtain the nondominated set of (accuracy, diversity). We have obtained the results of single objective optimization (i.e., optimizing either accuracy or diversity) and compared them with the results of MOEA on sixteen UCI data sets. The results show that the MOEA can improve the performance of ensemble classifier. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stereo matching algorithm to solve environment sensing problems. It integrates a non-convex optical flow and Viterbi process. The non-convex optical flow employs a new adaptive weighted non-convex Total Generalized Variation (TGV) model, which can obtain sharp disparity maps. Structural similarity, total variation constraint, and a specific merging strategy are combined with the 4 bi-directional Viterbi process to improve the robustness. In the fusion of the optical flow and Viterbi process, a new occlusion processing method is incorporated in order to get more sharp disparity and more robust result. Extensive experiments are conducted to compare this algorithm with other state-of-the-art methods. Experimental results show the superiority of our algorithm. [ABSTRACT FROM AUTHOR]

FP-Growth algorithm is an algorithm of association rules that does not generate a set of candidate, so it has very high practical value in face of the rapid growth of data volume in wisdom medical treatment. Because FP-Growth is a memory-resident algorithm, it will appear to be powerless when it is used for massive data sets. The paper combines Hadoop and FP-Growth algorithm and through the actual analysis of traditional Chinese medicine (TCM) data compares the performance in two different environments of stand-alone and distributed. The experimental results show that FP-Growth algorithm has a great advantage in the processing and execution of massive data after the MapReduce parallel model, so that it will have better development prospects for intelligent medical treatment. [ABSTRACT FROM AUTHOR]

Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front. [ABSTRACT FROM AUTHOR]

In this paper, a load balancing mechanism based on cell breathing was proposed for the hybrid EPON-WiMAX network. The hybrid EPON-WiMAX network integrates the emerging Ethernet Passive Optical Network (EPON) and Worldwide Interoperability for Microwave Access (WiMAX) technologies to support high bandwidth, broadband access, mobility, and Quality of Service (QoS) guarantee. Load balancing is required due to uneven distribution of traffic among different WiMAX base stations (BS) as well as mobility of WiMAX Subscriber Station (SS). Cell breathing is a well-known load balancing technique adopted in wireless networks. By changing the transmission powers of the base stations, the cell breathing technique is able to adjust the traffic, both uplink and downlink, load of base stations. In the integrated EPON-WiMAX network, transmission powers of WiMAX base stations can be optimally allocated at the EPON optical networking unit (ONU). In this paper, we formulate the cell breathing-based load balancing problem into a linear programming problem. Our goal is to find the best power adjustment that maximizes system throughput. To alleviate the time complexity of solving the linear programming problem, we propose a Heaviest Load First Algorithm (HLFA) to obtain the near optimal solution. The solution obtained by the HLFA algorithm will not waste any transmission power to make the entire system energy-efficiency. We demonstrate the performance of HLFA via extensive simulations. The simulation results show that the HLFA can provide the best solution to achieve load balancing and enhance the system throughput as compared to existing solutions. Furthermore, HLFA maintains good performance even when the system has multiple overloaded ONU-BSs. [ABSTRACT FROM AUTHOR]

In this paper, signed distance of Symmetrical Intuitionistic Fuzzy Numbers (SIFNs) is introduced. Based on this signed distance and the crisp ranking system on real numbers, a new ranking system for SIFNs is defined, which seems to be very realistic. To illustrate the applicability and suitability of the proposed ranking method and to deal with ambiguity and imprecision, one of the vital mathematical programming problem viz. Solid Transportation Problem (STP) is formulated in intuitionistic fuzzy environment. A new method has been proposed to compute initial basic feasible solution for the same. Also the significance of the proposed approach over existing methods is illustrated. Finally numerical examples are solved to demonstrate the efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

The aim of this paper is to use the concept of extended linguistic variables to develop a new methodology for solving multiattribute group decision making problems under linguistic assessments. In this methodology, using the generalized induced ordered weighted averaging operator and extended linguistic variables, linguistic ratings of each alternative on all attributes for each decision maker are aggregated into a comprehensive assessment, which is not always a linguistic term in the linguistic term set designated a priori. Comprehensive assessments of each alternative on all decision makers are similarly aggregated into a comprehensive assessment of the alternative for the group. An optimal linguistic term in the linguistic term set which approaches the comprehensive assessment of each alternative for the group as close as possible is computed and an extended 2-tuple fuzzy linguistic representation is obtained for the alternative. The ranking order of alternatives for the group is generated according to the extended 2-tuple fuzzy linguistic representations. A numerical example is examined to demonstrate applicability and implementation process of the methodology proposed in this paper. [ABSTRACT FROM AUTHOR]

There exists little investigation on multiattribute decision making under intuitionistic fuzzy environments although both crisp and fuzzy multiattribute decision making have achieved a great progress. In this paper, multiattribute decision making problems using intuitionistic fuzzy sets are investigated and the TOPSIS is further extended to develop one new methodology for solving such problems. In this methodology, an interval fractional programming model is constructed on the basis of the relative closeness coefficient using the TOPSIS. Comprehensive evaluation of each alternative, which may be described as an intuitionistic fuzzy set or interval number, is calculated using two auxiliary mathematical programming problems derived from the interval fractional programming model proposed in this paper. Optimal degrees of membership for alternatives are calculated to determine their ranking order using the concept of likelihood based on the ranking method of interval numbers. Implementation process of the method proposed in this paper is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Planar quadratic differential systems occur in many areas of applied mathematics. Although more than one thousand papers were written on these systems, a complete understanding of this class is still missing. Classical problems, and in particular, Hilbert's 16th problem [Hilbert,1900], are still open for this class. Even when not dealing with limit cycles, still some problems have remained unsolved like a complete classification of different phase portraits without limit cycles. For some time it was thought (see [Coppel, 1966]) there could exist a set of algebraic functions whose signs would completely determine the phase portrait of a quadratic system. Nowadays we already know that this is not so, and that there are some analytical, nonalgebraic functions that also play a role when dealing with limit cycles and separatrix connections. However, it is possible to find out a set of algebraic functions whose signs determine the characteristics of all finite and infinite singular points. Most of the work up to now has dealt with this problem studying it using different normal forms adapted to some subclasses of quadratic systems. A general work useful for any quadratic system regardless of affine changes has only been done for the study of infinite singular points [Schlomiuk et al., 2005]. In this paper, we give a complete global classification of quadratic differential systems according to their topological behavior in the vicinity of the finite singular points. Our classification Main Theorem gives us a complete dictionary describing the local behavior of finite singular points using algebraic invariants and comitants which are a powerful tool for algebraic computations. Linking the result of this paper with the main one of [Schlomiuk et al., 2005] which uses the same algebraic invariants, it is possible to complete the algebraic classification of singular points (finite and infinite) for quadratic differential systems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a fuzzy linear programming technique for multidimensional analysis of preference (FLINMAP) in multiattribute group decision making problems with linguistic variables and incomplete preference information. In this paper, linguistic variables are used to assess an alternative on qualitative attributes using fuzzy ratings corresponding to some triangular fuzzy numbers. Each alternative is assessed on the basis of its distance to a fuzzy positive ideal solution (FPIS) which is unknown a priori. The FPIS and the weights of attributes are calculated by constructing a new linear programming model based on the group consistency and inconsistency indices defined on the basis of preferences between alternatives given by the decision makers. The distance of each alternative to the FPIS can be calculated to determine the ranking order of all alternatives. The implementation process of this methodology is demonstrated with an example. [ABSTRACT FROM AUTHOR]

Recent advances in solving constraint satisfaction problems (CSPs) and heuristic search have made it possible to solve classical planning problems significantly faster. There is an increasing amount of work on extending these advances to solving planning problems in more expressive languages. These problems and languages contain metric time, quantifiers and resource quantities. SAT-based approaches are very effective at optimal planning. In this paper we report on SAT-based temporal planner T-SATPLAN. One key challenge in the development of planners casting planning as SAT or CSP is the identification of constraints which are satisfied if and only if there is a plan of k steps. In this paper we show how such a SAT encoding can be synthesized for temporal planning. As part of this, we generalize explanatory frame axioms for two states of fluents (true, false) to three states of fluents (true, false and undefined). We show how this SAT encoding can be simplified. We discuss two additional SAT encodings of temporal planning. The encoding schemes make it easier to exploit progress in SAT and CSP solving to solve temporal planning problems. We also report on an experimental evaluation of T-SATPLAN using one such encoding scheme. The evaluation shows that significantly large SAT encodings of temporal planning problems can be solved extremely fast. [ABSTRACT FROM AUTHOR]

In the discrete-valued input control of dynamical systems, quantizers, which convert continuous-valued signals into discrete-valued ones, play an important role. This paper focuses on the design problem of dynamic quantizers with fixed-structures such as a fixed-order and a decentralized structure. It is known to be difficult to solve the problem since it is NP-hard. Therefore, in order to solve the problem in practical steps, we adopt differential evolution (DE) which is one of the evolutionary computation algorithms. We first propose a DE-based design method of quantizers with fixed structures. Then, the effectiveness of the proposed method is verified by examples in the discrete-valued input control of dynamical systems. Moreover, we compare the performance of the proposed DE-based method with that of a particle swarm optimization (PSO)-based method, and show that DE is an easy-to-use and reliable tool for quantizer design problem. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark. [ABSTRACT FROM AUTHOR]

Research in optimization under uncertainty is alive. It assumes different shapes and forms, all concurring to the general goal of designing effective and efficient tools for handling imprecision in an Optimization setting. In this paper we present a new approach for dealing with multiobjective programming problems with fuzzy objective functions. Similar to many approaches in the literature, our approach relies on the deffuzification of involved fuzzy quantities. Our improvement stem from the choice of a deffuzification operator that captures essential features of fuzzy parameters at hand rather than those that yield single values, leading to a loss of many useful information. Two oracles play a pivotal role in the proposed method. The first one returns a near interval approximation to a given fuzzy number. The other one delivers a Pareto Optimal solution of the resulting multiobjective program with interval coefficient. A numerical example is also provided for the sake of illustration. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction. [ABSTRACT FROM AUTHOR]

A reduction transforms a binary picture only by changing some black points to white ones, which is referred to as deletion. Sequential reductions traverse the black points of a picture, and consider a single point for possible deletion, while parallel reductions can delete a set of black points simultaneously. Two reductions are called equivalent if they produce the same result for each input picture. A deletion rule is said to be equivalent if it yields a pair of equivalent parallel and sequential reductions. This paper introduces a class of equivalent deletion rules that allows us to establish a new sufficient condition for topology-preserving parallel reductions in arbitrary binary pictures. In addition we present a method of verifying that a deletion rule given by matching templates is equivalent, a necessary and sufficient condition for order-independent deletion rules, and a sufficient criterion for order-independent and translation-invariant parallel subfield-based algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose an approach for multiobjective programming problems with fuzzy number coefficients. The main idea behind our approach is to approximate involved fuzzy numbers by their respective nearest interval approximation counterparts. An algorithm that returns a nearest interval approximation to a given fuzzy number, plays a pivotal role in the proposed method. Our approach contrasts markedly with those based on deffuzification operators which replace a fuzzy set by a single real number leading to a loss of many important information. A numerical example is also provided for the sake of illustration. [ABSTRACT FROM AUTHOR]

In this paper, a digitally programmable OTA-based multi-standard receiver baseband chain is presented. The multi-standard receiver baseband chain consists of two programmable gain amplifiers (PGA1 and PGA2) and a fourth-order LPF. The receiver is suitable for Bluetooth/ UMTS/DVB-H/WLAN standards. Three different programmable OTA architectures based on second generation current conveyors (CCIIs) and Current Division Networks (CDNs) are dis-cussed. The programmable OTA with the lowest power consumption, moderate area and good linearity -- better than -50 dB HD3 -- is selected to realize the multi-standard baseband receiver chain. The power consumption of the receiver chain is 6 mW. The DC gain varies over a 68 dB range with 1 MHz to 13.6 MHz programmable bandwidth. The receiver baseband chain is realized using 90 nm CMOS technology model under ±0.5 V voltage supply. [ABSTRACT FROM AUTHOR]

Community structure detection is widely accepted as a means of elucidating the functional properties of complex networks. The problem statement is ever evolving, with the aim of developing more flexible and realistic modeling procedures. For example, a first step in developing a more informative model is the inclusion of weighted interactions. In addition to the standard community structure problem, interest has increased in the detection of overlapping communities. Adopting such constraints may, in some cases, represent a more true to life abstraction of the system under study. In this paper, two novel mathematical programming algorithms for module detection are presented. First, disjoint modules in weighted and unweighted networks are detected by formulating modularity maximization as a mixed integer nonlinear programming (MINLP) model. The solution obtained is then used to detect overlapping modules through a further MINLP model. The inclusion of two parameters controlling the extent of overlapping offers flexibility in user requirements. Comparative results show that these methodologies perform competitively to previously proposed methods. The methodologies proposed here promote the detection of topological relationships in complex systems. Together with the amenable nature of mathematical programming models, we show that both algorithms offer a versatile solution to the community detection problem. [ABSTRACT FROM AUTHOR]

Real-world dispute resolution should be guided by laws, even if such disputes may be resolved by bodies other than the court of laws. Hence in order to build contract dispute resolution systems we need a tool capable of representing, reasoning and programming with contract laws. In this paper we present such a tool called MoDiSo (MOdular Argumentation for DIspute ReSOlution ) which combines the strengths of state-of-the-art argumentation-based techniques for different aspects of law, to propose: first, a modular architecture for contract dispute resolution systems with an edit-compile-dispute loop facilitating incremental system developments; and second, a methodology to represent and reason with legal doctrines in contract laws in the formal language of assumption-based argumentation. We demonstrate the tool with several legal doctrines for performance relief in common law of contracts. As a by-product, we obtain a dispute resolution system capable of explaining legal outcomes by automatically generating relevant arguments. [ABSTRACT FROM AUTHOR]

In this paper, we introduce dual g-frames for a closed subspace in a separable Hilbert space and also give a characterization. Generally dual g-frames for a closed subspace are non-commutative. Therefore, we construct dual g-frames for closed subspaces from two aspects and give the corresponding formulas, respectively. Finally, we give a necessary and sufficient condition for commutative dual g-frame pairs for closed subspaces under certain conditions. [ABSTRACT FROM AUTHOR]

Recently Ganesan and Veeramani introduced a new approach for solving a kind of linear programming problems involving symmetric trapezoidal fuzzy numbers without converting them to the crisp linear programming problems. But their approach is not efficient for situations in which some or all variables are restricted to lie within fuzzy lower and fuzzy upper bounds. In this paper, by a natural extension of their approach we obtain some new results leading to a new method to overcome this shortcoming. [ABSTRACT FROM AUTHOR]

By using a time-domain approach, this paper analyzes the chaotic dynamics of the fractional-order system obtained by coupling two fractional Lorenz systems. The work exploits the Adomian decomposition method (ADM), shows that the proposed fractional system with order as low as 2.88 exhibits a novel four-wing chaotic attractor. The existence of chaos is also confirmed by the application of the "0-1 test" for chaos. Finally, an analysis of the system equilibria is conducted, which shows that the fractional-order four-wing attractor is truly the counterpart of the recently introduced integer-order four-wing attractor. [ABSTRACT FROM AUTHOR]

The intuitionistic fuzzy set (IFS) introduced by Atanassov in 1986 is characterized by two functions and has rarely been applied to the game theory yet. The aim of this paper is to develop the concept and methodology of matrix games with payoffs of Atanassov's IFSs. In this methodology, the concepts of the solutions for matrix games with payoffs of Atanassov's IFSs are defined based on the degree of membership and the degree of non-membership. It is proven that matrix games with payoffs of Atanassov's IFSs have solutions, which can be obtained through solving a pair of nonlinear programming models derived from two auxiliary nonlinear bi-objective programming models. The proposed method is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Fuzzy mathematics is a generalization in which fuzzy numbers replace real numbers and fuzzy arithmetic replaces real arithmetic. It is an excellent scope for modeling vague and uncertain aspects of the actual environments. In this important area, Dubois and Prade1 defined a fuzzy matrix as a rectangular array of fuzzy numbers. They have also defined the LR type fuzzy numbers with some useful approximate arithmetic operators. The aim of this paper is to extend some useful aspects of linear algebra e.g. determinant, norm and eigenvalue for fuzzy matrices with LR fuzzy number entries by the use of fuzzy arithmetic. Finally, applications in fuzzy analytical hierarchy process (AHP) are investigated. [ABSTRACT FROM AUTHOR]

Several classification problems involve more than two classes. These problems are known as multiclass classification problems. One of the approaches to deal with multiclass problems is their decomposition into a set of binary problems. Recent work shows important advantages related with this approach. Several strategies have been proposed for this decomposition. The strategies most frequently used are All-vs-All, One-vs-All and Error Correction Output Codes (ECOC). ECOCs are based on binary words (codewords) and have been adapted to deal with multiclass problems. For such, they must comply with a number of specific constraints. Different dimensions may be adopted for the codewords for each number of classes in the problem. These dimensions grow exponentially with the number of classes present in a dataset. Two methods to choose the dimension of a ECOC, which assure a good trade-off between redundancy and error correction capacity, are proposed in this paper. The proposed methods are evaluated in a set of benchmark classification problems. Experimental results show that they are competitive with other multiclass decomposition methods. [ABSTRACT FROM AUTHOR]

Mutation in DNA is the principal cause for differences among human beings, and Single Nucleotide Polymorphisms (SNPs) are the most common mutations. Hence, a fundamental task is to complete a map of haplotypes (which identify SNPs) in the human population. Associated with this effort, a key computational problem is the inference of haplotype data from genotype data, since in practice genotype data rather than haplotype data is usually obtained. Different haplotype inference approaches have been proposed, including the utilization of statistical methods and the utilization of the pure parsimony criterion. The problem of haplotype inference by pure parsimony (HIPP) is interesting not only because of its application to haplotype inference, but also because it is a challenging NP-hard problem, being APX-hard. Recent work has shown that a SAT-based approach is the most efficient approach for the problem of haplotype inference by pure parsimony (HIPP), being several orders of magnitude faster than existing integer linear programming and branch and bound solutions. This paper provides a detailed description of SHIPs, a SAT-based approach for the HIPP problem, and presents comprehensive experimental results comparing SHIPs with all other exact approaches for the HIPP problem. These results confirm that SHIPs is currently the most effective approach for the HIPP problem. [ABSTRACT FROM AUTHOR]

The data staging problem involves positioning data within a distributed heterogeneous computing environment such that programs can access the requested data faster. This problem exists because applications constantly need up-to-date information to enable users to make decisions. In addition, these requests for information are normally occurring in an oversubscribed network. In such a heterogeneous distributed computing environment, each data storage location and intermediate node may have different data available, storage limitations, and communication links available. Sites in the heterogeneous network request data items and each request has an associated deadline and priority. This work extends the research presented in [ThT00] where a basic version of the data staging problem with static information was presented. This work introduces three new cost criteria and two new bounds on performance that were designed taking into account results from [ThT00]. A subset of the possible procedure/cost criterion combinations are evaluated in simulation studies considering a different priority weighting scheme, different average number of links used to satisfy each data request, and different network loadings, than was considered in [ThT00]. This paper also introduces a variable time, variable accuracy approach for using data items with "more desirable" and "less desirable" versions. [ABSTRACT FROM AUTHOR]

This paper investigates the synchronization of chaotic systems subject to parameter uncertainties. Based on the fuzzy-model-based approach, a switching controller will be proposed to deal with the synchronization problem. The stability conditions will be derived based on the Lyapunov approach. The tracking performance and parameter design of the proposed switching controller will be formulated as a generalized eigenvalue minimization problem which can be solved numerically using some convex programming techniques. Simulation examples will be given to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

General functional decomposition is mainly perceived as a logic synthesis method for implementing Boolean functions into FPGA-based architectures. However it also has important applications in many other fields of modern engineering and science. In this paper, advantages of functional decomposition are demonstrated on "real life" examples. Application of decomposition-based methods in other fields of modern engineering is presented. In the case of decision tables, application of decomposition methods leads to significant benefits in the analysis process of data dependencies, especially in cases when the input decision tables are unmanageably large. Experimental results demonstrate that it can help implementing sequential machines using flip-flops or ROM memory. It also can be efficiently used as multilevel logic synthesis method for VLSI technology. [ABSTRACT FROM AUTHOR]

Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regression model. In addition, a numerical example is used to demonstrate the proposed method and compare with other methods. On the basis of the results, we can conclude that the proposed method can provide a correct fuzzy regression model and avoid the problem of multicollinearity. [ABSTRACT FROM AUTHOR]

Tandem queueing models play an important role in many real world systems such as computer systems, production lines, and service systems. This paper proposes a procedure to construct the membership functions of the performance measures in tandem queueing systems, in that the arrival rate and service rates are fuzzy numbers. The basic idea is to transform a fuzzy tandem queue to a family of crisp tandem queues by applying the α -cut approach. Then on the basis of α -cut representation and the extension principle, a pair of mathematical programs is formulated to describe this family of crisp tandem queues, via which the membership functions of the performance measures are derived. Two numerical examples are solved successfully to demonstrate the validity of the proposed approach. Since the performance measures are expressed by membership functions rather than by crisp values, the fuzziness of input information is completely conserved. Thus the proposed approach for fuzzy systems can represent the system more accurately, and more information is provided for designing queueing systems. The successful extension of tandem queues to fuzzy environments permits tandem queueing models to have wider applications. [ABSTRACT FROM AUTHOR]

This paper introduces a new ensemble technique, cluster-based concurrent decomposition (CBCD) that induces an ensemble of classifiers by decomposing the training set into mutually exclusive sub-samples of equal-size. The CBCD algorithm first clusters the instance space by using the K-means clustering algorithm. Afterwards it produces disjoint sub-samples using the clusters in such a way that each sub-sample is comprised of tuples from all clusters and hence represents the entire dataset. An induction algorithm is applied in turn to each subset, followed by a voting mechanism that combines the classifier's predictions. The CBCD algorithm has two tuning parameters: the number of clusters and the number of subsets to create. Using a suitable meta-learning it is possible to tune these parameters properly. In the experimental study we conducted, the CBCD algorithm, using an embedded C4.5 algorithm, outperformed the bagging algorithm of the same computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we concentrate on two kinds of fuzzy linear programming problems: linear programming problems with only fuzzy resource variables and linear programming problems in which both the resource variables and the technological coefficients are fuzzy numbers. We consider here only the case of fuzzy numbers with modified s-curve membership functions. We propose here the modified s-curve membership function as a methodology for fuzzy linear programming and use it for solving these problems. We also compare the new proposed method with non-fuzzy linear programming optimization method. Finally, we provide real life application examples in production planning and their numerical solutions. [ABSTRACT FROM AUTHOR]

Image restoration problems can naturally be cast as constrained convex programming problems in which the constraints arise from a priori information and the observation of signals physically related to the image to be recovered. In this paper, the focus is placed on the construction of constraints based on wavelet representations. Using a mix of statistical and convex-analytical tools, we propose a general framework to construct wavelet-based constraints. The resulting optimization problem is then solved with a block-iterative parallel algorithm which offers great flexibility in terms of implementation. Numerical results illustrate an application of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered. In this regard, we define risk-neutral, risk-averse and risk-seeking fuzzy linear regression models. In order to do that, two equality indices are applied to express the degree of equality between a pair of fuzzy numbers. We also develop three mathematical models to obtain the parameters of fuzzy linear regression models. Minimizing the difference between the total spread of the observed and estimated values is the objective of these models. The advantage of our proposed models is the simplicity in programming and computation. [ABSTRACT FROM AUTHOR]

In the Weighted Ring Arc-Loading Problem with Integer Splitting, we are given a set of communication requests each associated with a non-negative integer weight. The problem is to find a routing scheme such that the maximum load on arcs of the ring is minimized, subject to that the weight of each request may be split into two integral parts routed in two directions around the ring, where the load of an arc is the sum of parts routed through the arc. A pseudo-polynomial algorithm for this problem is implied by a result in [C. Wilfong and P. Winkler, Ring routing and wavelength translation, Proceedings of the 9th ACM-SIAM Symposium on Discrete Algorithms, San Francisco, CA, 1998, 333-341]. By refining the rounding technique developed in the same paper, we prove that the problem can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques. [ABSTRACT FROM AUTHOR]

This paper presents a procedural framework that unifies various mechanisms to look for discrete-neighborhood saddle points in solving discrete constrained optimization problems (DCOPs). Our approach is based on the necessary and sufficient condition on local optimality in discrete space, which shows the one-to-one correspondence between the discrete-space constrained local minima of a problem and the saddle points of the corresponding Lagrangian function. To look for such saddle points, we study various mechanisms for performing ascents of the Lagrangian function in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our results show that CSAEA, a combined constrained simulated annealing and evolutionary algorithm, performs well when using mutations and crossovers to generate trial points and accepting them based on the Metropolis probability. We apply iterative deepening to determine the optimal number of generations in CSAEA and show that its performance is robust with respect to changes in population size. To test the performance of the procedures developed, we apply them to solve some continuous and mixed-integer nonlinear programming (NLP) benchmarks and show that they obtain better results than those of existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the flexible job shop scheduling problem. We propose an efficient heuristic method for solving the assignment problem. Indeed, we propose a worst case analysis to evaluate the performance of such a heuristic. The second specificity of the problem studied is the sequencing property. Our approach consists in the application of an evolutionary algorithm based on a set of adapted operators to solve the sequencing step. Some lower bounds for the problem (previously proposed in Ref. 1) will be used in order to evaluate the quality of our method and the solutions according to the different criteria. [ABSTRACT FROM AUTHOR]

We present a novel case-based plan recognition method that interprets observations of plan behavior using an incrementally constructed case library of past observations. The technique is novel in several ways. It combines plan recognition with case-based reasoning and leverages the strengths of both. The representation of a plan is a sequence of action-state pairs rather than only the actions. The technique compensates for the additional complexity with a unique abstraction scheme augmented by pseudo-isomorphic similarity relations to represent indices into the case base. Past cases are used to predict subsequent actions by adapting old actions and their arguments. Moreover, the technique makes predictions despite observations of unknown actions. This paper evaluates the algorithms and their implementation both analytically and empirically. The evaluation criteria include prediction accuracy at both an abstract and a concrete level and across multiple domains with and without case-adaptation. In each domain the system starts with an empty case base that grows to include thousands of past observations. Results demonstrate that this new method is accurate, robust, scalable, and general across domains. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to introduce a new type of fuzzy matrix games: fuzzy constrained matrix games. A computational method for its solution based on establishment of the auxiliary fuzzy linear programming for each player is proposed. The approach based on the multiobjective programming is established to solve these fuzzy linear programming. Effectiveness is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

A conventional portfolio selection problem, which is based on a mean-variance model, is difficult to solve by using mathematical programming techniques. This difficulty is caused by the fact that the corresponding mathematical programming problems are large-dimensional one, since almost all variance-covariances of return rates are, typically, not zeros. In this paper, we propose an efficient method for solving a portfolio selection problem, a method which uses a Boltzmann machine. In a real-life problem, it is also important to find the optimal combination of a small number of invested securities out of many securities in a market, because of a limited amount of funds to invest into securities. So we also propose a portfolio selection method to obtain the invest ratio of limited number of securities out of huge number of securities using a multi-stage application of the Boltzmann machine. [ABSTRACT FROM AUTHOR]