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This paper deals with the optimal formation control problem of multi-agent systems under a prior unknown desired shapes. The objective of this paper is to accomplish a rolling optimal formation of multi-agent systems by incorporating trajectory tracking and energy saving control into a unified framework. First, according to the real-time desired position offset between each follower and the leader, the rolling optimization performance indexes are introduced. Then, a distributed rolling optimization algorithm is proposed to realize the desired formation of multi-agent systems under the minimum energy consumption of agents. Finally, several simulation examples are conducted to illustrate the effectiveness of the proposed design algorithm. [ABSTRACT FROM AUTHOR]

In model predictive control (MPC), the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set (AS) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control (UFTOC) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this paper, the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS-type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speedup can be important for AS-type solvers used for linear, nonlinear, and hybrid MPC. [ABSTRACT FROM PUBLISHER]

Pareto optimality is the fundamental construct employed to determine whether a given solution to a multi-criteria mathematical optimization model is preferred to another solution. In this paper we describe an approach (Pattern Efficient Set Algorithm – PESA) to generating a pattern-efficient set of non-dominated vectors to a multi-objective optimization problem. Our approach incorporates an optimization model designed to yield certain non-dominated vectors that can fill gaps between already generated non-dominated vectors, providing a way to deal with the adjacency of generated non-dominated vectors and to quantify the gaps between them. We also propose a pseudo-randomized variant of PESA (rPESA) that randomly generates hypothetical bounds for the objective functions and uses them in the optimization model. To test our approach we selected ten problems from the literature, including bi-objective, 3-objective, 5-objective and 10-objective test instances with non-convex, disconnected or continuous Pareto. The inverted generational distance (IGD) and the hyper-volume (HV) are used as performance metrics to measure the quality of the obtained approximations. We also present graphically the numerical results from applying our method. [ABSTRACT FROM AUTHOR]

In networked control systems, selecting a subset of input and output nodes is a crucial step in designing a stabilizing controller. Most existing approaches to input and output selection focus on nominal systems with known parameters. For systems with uncertainties and time delays, current selection methods are based on exploiting the convexity after relaxing the original problem, which is inherently discrete, to continuous optimization forms, and hence lack optimality guarantees. This paper studies the problem of identifying the minimum-size sets of input and output nodes to guarantee stability of a linear system with uncertainties and time delays. We derive sufficient conditions to guarantee the existence of a stabilizing controller for an uncertain linear system, based on a subset of system modes lying within the controllability and observability subspaces induced by the selected inputs and outputs. We then formulate the problems of selecting minimum-size sets of input and output nodes to satisfy the derived conditions, and prove that they are equivalent to discrete optimization problems with bounded submodularity ratios. We develop polynomial-time selection algorithms with provable guarantees on the minimum number of inputs and outputs required. Our approach is applicable to various types of uncertainties, including additive uncertainty, multiplicative uncertainty, uncertain output delay, and structured uncertainty. In a numerical study, we test our approach for the wide-area damping control in power systems to ensure small signal stability. Our results are validated on the IEEE 39-bus test power system. [ABSTRACT FROM AUTHOR]

This paper considers the problem of a leader that seeks to optimally influence the opinions of agents in a directed network through connecting with a limited number of the agents (“direct followers”), possibly in the presence of a fixed competing leader. The settings involving a single leader and two competing leaders are unified into a general combinatoric optimization problem, for which two heuristic approaches are developed. The first approach is based on a convex relaxation scheme, possibly in combination with the $\ell _1$ -norm regularization technique, and the second is based on a greedy selection strategy. The main technical novelties of this work are in the establishment of supermodularity of the objective function and convexity of its continuous relaxation. The greedy approach is guaranteed to have a lower bound on the approximation ratio sharper than $(1-1/e)$ , while the convex approach can benefit from efficient (customized) numerical solvers to have practically comparable solutions possibly with faster computation times. The two approaches can be combined to provide improved results. In numerical examples, the approximation ratio can be made to reach ${\text{90}\%}$ or higher depending on the number of direct followers. [ABSTRACT FROM AUTHOR]

This paper is concerned with the recursive filtering problem for a class of networked linear time-varying systems subject to the scheduling of the random access protocol (RAP). The communication between the sensor nodes and the remote filter is implemented via a shared network. For the purpose of preventing the data from collisions, only one sensor node is allowed to get access to the network at each time instant. The transmission order of sensor nodes is orchestrated by the RAP scheduling, under which the selected nodes obtaining access to the network could be characterized by a sequence of independent and identically-distributed variables. The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant. The desired filter gain is calculated recursively by solving two Riccati-like difference equations. Furthermore, the boundedness issue of the corresponding filtering error covariance is investigated. Sufficient conditions are obtained to ensure the lower and upper bounds of the filtering error covariance. Two illustrative examples are given to demonstrate the correctness and effectiveness ofour developed recursive filtering approach. [ABSTRACT FROM AUTHOR]

Purpose – Multiphysical models are often useful for the design of electrical devices such as electrical machines. In this way, the modelling of thermal, magnetic and electrical phenomena by using an equivalent circuit approach is often used in sizing problems. The coupling of such models with other models is difficult to take into account, partly because it adds complexity to the process. The purpose of this paper is to propose an automatic modelling of thermal and magnetic aspects from an equivalent circuit approach, with its computation of gradients, using selectivity on the variables. Then, it discusses the coupling of various physical models, for the sizing by optimization algorithms. Sensibility analyses are discussed and the multiphysical approach is applied on a permanent magnet synchronous machine. Design/methodology/approach – The paper allows one to describe thermal and magnetic models by equivalent circuits. Magnetic aspects are represented by reluctance networks and thermal aspects by thermal equivalent circuits. From circuit modelling and analytical equations, models are generated, coupled and translated into computational codes (Java, C), including the computation of their Jacobians. To do so, model generators are used: CADES, Reluctool, Thermotool. The paper illustrates the modelling and automatic programming aspects with Thermotool. The generated codes are directly available for optimization algorithms. Then, the formulation of the coupling with other models is studied in the case of a multiphysical sizing by optimization of the Toyota PRIUS electrical motor. Findings – A main specificity of the approach is the ability to easily deal with the selectivity of the inputs and outputs of the generated model according to the problem specifications, thus reducing drastically the size of the Jacobian matrix and the computational complexity. Another specificity is the coupling of the models using analytical equations, possibly implicit equations. Research limitations/implications – At the present time, the multiphysical modelling is considered only for static phenomena. However, this limit is not important for numerous sizing applications. Originality/value – The analytical approach with the selectivity gives fast models, well adapted for optimization. The use of model generators allows robust programming of the models and their Jacobians. The automatic calculation of the gradients allows the use of determinist algorithms, such as sequential quadratic programming, well adapted to deal with numerous constraints. [ABSTRACT FROM AUTHOR]

Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques. [ABSTRACT FROM AUTHOR]

In this paper the comparison of a proposed neural network with generalized type-2 fuzzy weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural network with interval type-2 fuzzy weights (NNIT2FW) is presented. Generalized type-2 fuzzy inference systems are used to obtain the generalized type-2 fuzzy weights and are designed by a strategy of increasing and decreasing an epsilon variable for obtaining the different sizes of the footprint of uncertainty (FOU) for the generalized membership functions. The proposed method is based on recent approaches that handle weight adaptation using type-1 and type-2 fuzzy logic. The approach is applied to the prediction of the Mackey–Glass time series, and results are shown to outperform the results produced by other neural models. Gaussian noise was applied to the test data of the Mackey–Glass time series for finding out which of the presented methods in this paper shows better performance and tolerance to noise. [ABSTRACT FROM AUTHOR]

This paper proposes an optimum design method for two-dimensional heat conduction problem with heat transfer boundary condition based on the boundary element method (BEM) and the topology optimization method. The level set method is used to represent the structural boundaries and the boundary mesh is generated based on iso-surface of the level set function. A major novel aspect of this paper is that the governing equation is solved without ersatz material approach and approximated heat convection boundary condition by using the mesh generation. Additionally, the objective functional is defined also on the design boundaries. First, the topology optimization method and the level set method are briefly discussed. Using the level set based boundary expression, the topology optimization problem for the heat transfer problem with heat transfer boundary condition is formulated. Next, the topological derivative of the objective functional is derived. Finally, several numerical examples are provided to confirm the validity of the derived topological derivative and the proposed optimum design method. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to examine the relationships between processing conditions and magnetic properties of cores made of Soft Magnetic Composite (SMC) Somaloy 500. Design/methodology/approach – The effects of compaction pressure and hardening temperature may be combined considering SMC density. This quantity may be chosen for optimization of properties of ready-made cores. In order to describe hysteresis loops the phenomenological model based on hyperbolic tangent transformation is applied. Findings – SMC density affects substantially the shape of hysteresis loop. The paper provides a number of charts useful for checking how the parameters of the hysteresis model are affected. Research limitations/implications – The present study considers just one composition of the SMC and one type of lubricant. Future research shall be devoted to verification of the approach on a wider class of SMCs. Practical implications – Material density may be a relevant quantity in optimization of magnetic properties of ready-made SMC cores. The simple hysteresis model based on the, “effective field” concept and Takács’ idea of hyperbolic tangent transformation may be useful for description of hysteresis curves of SMC cores. Model parameters are sensitive against variations of material density. Originality/value – The results of the analysis may be useful for designers of magnetic circuits made of SMCs. [ABSTRACT FROM AUTHOR]

It is well known that one of the basic advantages of evolutionary programming is in deciding the most suitable place for breeding offspring and finding the route toward the global minimum. To reach this goal, the algorithm needs to estimate the coordinates of the global minimum and then steer the new point toward it. In this paper, the estimation of the global minimum is calculated by weighted mean coordination of individuals (WMP), and then a road is mapped between the coordinates of the parents and the WMP. In the proposed method, fuzzy logic is used for deciding on the road and the best coordination to breed offspring. The proposed algorithm is tested on 65 well-known cost functions and is compared with five algorithms inside the EP family. In the next section of the paper, the algorithm is tested on the high-dimensional problem of modeling ozone layer data, which includes almost 26,000 unknown parameters. The results demonstrate the capability of the proposed method in having acceptable speed and accuracy. [ABSTRACT FROM AUTHOR]

Purpose This paper compares six reliability-based design optimization (RBDO) approaches dealing with uncertainties for a simple mathematical model and a multidisciplinary optimization problem of a safety transformer to highlight the most effective.Design/methodology/approach The RBDO and various approaches to calculate the probability of failure are is presented. They are compared in terms of precision and number of evaluations on mathematical and electromagnetic design problems.Findings The mathematical example shows that the six RBDO approaches have almost the same results except the approximate moment approach that is less accurate. The optimization of the safety transformer highlights that not all the methods can converge to the global solution. Performance measure approach, single-loop approach and sequential optimization and reliability assessment (SORA) method appear to be more stable. Considering both numerical examples, SORA is the most effective method among all RBDO approaches.Originality/value The comparison of six RBDO methods on the optimization problem of a safety transformer is achieved for the first time. The comparison in terms of precision and number of evaluations highlights the most effective ones. [ABSTRACT FROM AUTHOR]

This paper deals with the design, performance, and robustness analysis of nonlinear attitude complementary filters on \textSO(3). We derive explicit time solutions of the attitude estimation error dynamics of the filters (in the disturbance-free case) and analyze their performance and robustness with respect to measurement disturbances. The stability and performance properties of the filters can be easily deduced from the obtained closed-form solutions. A new class of attitude complementary filters on \textSO(3) with state-dependent gains is proposed and shown to exhibit improved performance and robustness properties compared to the fixed-gain traditional complementary filter on \textSO(3). [ABSTRACT FROM PUBLISHER]

Alternation was first proposed by Chandra et al. for obtaining a theoretical model of parallel computations. Compared with nondeterminism, alternation gives computing devices the power of universal choice in addition to existential choice. In this paper, we put forward a notion of fuzzy alternating Büchi automata over a distributive lattice L ( L -ABAs, for short). In our setting, a weight is the label of a leaf node of the run tree when executing a transition, which is helpful in complementing L -ABAs. Taking the dual operation on the transition function and negating final costs on states, we can get the complement of a given L -ABA. We point out that L -ABAs have the same expressive power as L -valued fuzzy nondeterministic Büchi automata ( L -NBAs). A construction presented here shows that languages accepted by L -valued fuzzy alternating co-Büchi automata ( L -ACAs) are also ω -regular languages. Moreover, closure properties of L -ABAs and decision problems for L -ABAs are also discussed. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs) that is inspired by the collective foraging behavior of real honey bee colonies. Due to its foraging model and its solution search equation, ABC generally performs well in exploration but badly in exploitation. To address this concerning issue and obtain a good balance between exploration and exploitation, in this paper, we mainly introduce into the ABC an adaptive method for the population size (AMPS). AMPS is inspired by the natural principle that the size of a population is affected by the availability of food resources. When food resources are abundant, a population tends to expand; otherwise, a decrease in the availability of food resources leads to a shrinkage in the population size. Specifically, when the algorithm performs well in exploration, AMPS will shrink the population to enhance exploitation by periodically removing some inferior solutions that have low success rates. In contrast, AMPS will enlarge the population to improve exploration by introducing some reserved solutions. Furthermore, to make AMPS perform better, we design a new solution search equation for employed bees and onlooker bees. Moreover, we also improve the probability model of the onlooker bees. By embedding our three proposed algorithmic components into ABC, we propose a novel ABC variant, called APABC. To demonstrate the performance of APABC, we compare APABC with some state-of-the-art ABC variants and some other non-ABC methods on 22 scalable benchmark functions and 30 CEC2014 test functions. The simulation results show that APABC is better than or at least competitive with the competitors in terms of its solution quality, robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Abstract: Biogeography-Based Optimization (BBO), inspired by the science of biogeography, is a novel population-based Evolutionary Algorithm (EA). For optimization problems, BBO builds the matching mathematical model of the organism distribution. In this evolutionary mechanism, species migrating among islands can be considered as the information transition among different solutions represented by habitats. Solutions are reassembled according to migration rates. However, so far, the migration models are generally designed by empirical studies. This leads to immature conclusions that are unreliable. To complete the previous works, this paper investigates transition probability matrices of BBO to clarify that the transition probability of median number of species is not the only determinant factor to influence performance. The impact of migration rates on BBO is mathematically discussed, which is helpful to design migration models. Using numerical simulations, the BBO and several other classical evolutionary algorithms are compared. The simulations also comprehensively explain the effect of the BBO’s properties on its performance including dimension, population size, and migration models. The results validate the theoretical analysis in this paper. [Copyright &y& Elsevier]

As a special case of information table, multi-scale decision table can usually be observed in real-life world. In such table, objects may take different values under the same attribute measured at different scales. Based on inclusion relation of subsets of attributes and coarse relation of scales of attributes, multi-layered granulations and stratified rough set approximations in multi-scale decision tables are shown from the perspective of granular computing. Compared with a special case studied by Wu and Leung, the multi-scale decision tables of diverse attributes with different numbers of levels of scales are studied in this paper. Furthermore, complement model and lattice model are proposed to analyze the optimal scale selection for multi-scale decision tables. Correspondingly, algorithms of the two models are designed and some experiments are performed to testify feasibilities of these proposed algorithms and to make comparisons of the models. [ABSTRACT FROM AUTHOR]

Based on the alternating direction method of multipliers, in this paper, we propose, analyze, and test a coupled linearized method, which aims to minimize an unconstrained problem consisting of a loss term and a regularization term in an online setting. To solve this problem, we first transform it into an equivalent constrained minimization problem with a separable structure. Then, we split the corresponding augmented Lagrangian function and minimize the resulting subproblems distributedly with one variable by fixing another one. This method is easy to execute without calculating matrix inversion by implementing three linearized operations per iteration, and at each iteration, we can obtain a closed-form solution. In particular, our update rule contains the well-known soft-thresholding operator as a special case. Moreover, upper bound on the regret of the proposed method is analyzed. Under some mild conditions, it can achieve O(1/\sqrt T) convergence rate for convex learning problems and O((log T)/ T) for strongly convex learning. Numerical experiments and comparisons with several state-of-the-art methods are reported, which demonstrate the efficiency and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Abstract: Many computational- or rational-level theories of human cognition suffer from computational intractability: the postulated optimization functions are impossible to compute in a reasonable time by a finite mind/brain, or any other computational mechanism. It has been proposed that such intractable theories can nevertheless have explanatory force if we assume that human cognitive processes somehow approximate the optimal function. This raises the question of when a cognitive process can be said to approximate an optimal function. In this paper, we distinguish between two notions of approximation, called value-approximation and structure-approximation respectively, and show that they are not equivalent. Although a mathematical framework for assessing degrees of tractable value-approximability has long been available, no such framework previously existed for structure-approximability. In this paper, we present a framework consisting of definitions and proof techniques for assessing degrees of structure-approximability. We illustrate the use of our framework for a particular intractable cognitive theory, i.e., Coherence model, known to be equivalent to harmony maximization in Hopfield networks. We discuss implications of our findings for this class of theories, as well as explain how similar results may be derived for other intractable optimization theories of cognition. [Copyright &y& Elsevier]

Abstract: Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min–max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper. [Copyright &y& Elsevier]

Abstract: This paper provides a characterization of fairness concepts in network resource allocation problems from the viewpoint of information theory. The fundamental idea underlying this paper is to characterize the utility functions in optimization problems, which motivate fairness concepts, based on a trade-off between user and network satisfaction. User satisfaction is evaluated using information divergence measures, which were originally used in information theory, to evaluate the difference between an implemented resource allocation and a requested resource allocation. The requested resource allocation is assumed to be ideal in some sense from the user’s point of view. Network satisfaction is evaluated based on the efficiency of resource usage in the implemented resource allocation. The results in this paper indicate that the well-known fairness concept called weighted α-proportional fairness can be characterized using the α-divergence measure, which is a general class of information divergence measures, as an equilibrium of the trade-off. Some typical examples of applications demonstrate how the presented characterization works. [Copyright &y& Elsevier]

Purpose - The aim of the paper is to present the theory and algorithms based on the methods of systems optimal control for a numerical solution of a defined mathematical model of a system as well as that of a mathematical model of game theory. Design/methodology/approach - The paper brings a formulation of the mathematical model of a problem of systems optimal control with distributed parameters in Hilbert space. The mathematical model of the optimal control problem includes equations that also occur in the defined mathematical model of the theory of a two player zero-sum game. Optimization problems of game theory have been defined for the purpose of finding a saddle point of a functional satisfying task constraints ?>0. Findings - In order to find a saddle point of a functional and that one of a functional with a limitation, a designed algorithm of an iterative gradient method is presented. Furthermore, the paper contains a concept of algorithms designing that can be applied to a numerical solution of the defined problem of game theory. These algorithms can be realized on the basis of the methods of systems optimal control. After an adjoint state of the system is defined, a saddle point of a functional will be characterized by equations and inequalities. Originality/value - The contribution of the paper lies in the formulation of the theorems which express the necessary and sufficient conditions of optimality for saddle points of a functional. Furthermore, it has been proved that algorithms of methods of systems optimal control with distributed parameters can be used for the solution of a mathematical model of game theory. The paper contains original results achieved by the authors within scientific projects. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to present a method of modeling the variable reluctance stepper motor using the time-stepping finite element technique. The proposed model is used to obtain the optimal control law for the input circuit solving the linear-quadratic problem. Design/methodology/approach - A strongly coupled field-circuit model of the stepper motor is presented. Also, the method of the optimal control that minimizes the power loss in the motor windings is proposed. Findings - The proposed optimal control method can be applied to the electrical machines connected to the electronic converters. Calculated control signals may be used to obtain the optimal waveforms of the input voltages at each phase of the analyzed machine. Originality/value - The paper examines the application of the presented control method to minimize the power loss in the stator windings of the four-phased variable reluctance stepper motor. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to establish the optimization model and solve the short-term economic dispatch of cascaded hydro-plants. Design/methodology/approach – An improved particle swarm optimization (IPSO) approach is proposed to solve the short-term economic dispatch of cascaded hydroelectric plants. The water transport delay time between connected reservoirs is taken into account and it is easy in dealing with the difficult hydraulic and power coupling constraints using the proposed method in practical cascaded hydroelectric plants operation. The feasibility of the proposed method is demonstrated for actual cascaded hydroelectric plant. Findings – The simulation results show that this approach can prevent premature convergence to a high degree and keep a rapid convergence speed. Research limitations/implications – The optimal values of parameters in the proposed method are the main limitations where the method will be applied to the economic operation of the hydro-plant. Practical implications – The paper presents useful advice for short-term economic operations of the hydro-plant. A new optimization method to solve the short-term optimal generation scheduling is proposed. The optimal generation power and water discharge during the whole dispatching time for hydro-plant operation can be obtained. Originality/value – The IPSO method is realized by maintaining high diversity of the swarm during the optimization process and preventing premature convergence. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a bi-level programming optimization model to reduce traffic congestion of transportation network while evacuating people to safe shelters during disasters or special events. Design/methodology/approach – The previous optimization model for contra flow configuration only considered the character of the manager. However, the traffic condition is not only controlled by managers, but also depended on the root choice of travelers. A bi-level programming optimization model, which considered managers and evacuees' character, is proposed to optimize the contra flow of transportation network in evacuation during special events. The upper level model aims to minimize the total evacuation time, while the lower level based on user equilibrium assignment. A solution method based on discrete particle swarm optimization and Frank-Wolfe algorithm is employed to solve the bi-level programming problem. Findings – It is found that the bi-level programming based contra flow optimization model can improve evacuation efficiency and decrease evacuation time 30 per cent or more. With the increase of traffic demand, the evacuation time will decrease significantly by contra flow configuration. Research limitations/implications – In the optimization model, the background traffic is ignored for simplification and the contra flow is configured absolutely as 0 or 1, which ensures vehicles do not go back into the evacuation area. Practical implications – An efficient optimization model for traffic managers to reduce congestion and evacuation time of evacuation network. Originality/value – The new bi-level programming model not only considers managers' character, but also considers evacuees' reaction. The paper is aimed to optimize contra flow for transportation network. [ABSTRACT FROM AUTHOR]

this paper we deal with the finite-time stability problem for quadratic systems. Such class of systems plays an important role in the modeling of a wide class of nonlinear processes (electrical, robotic, biological, etc.). The main results of the paper consist of two sufficient conditions for finite-time stability analysis and finite-time stabilization via static state feedback; both conditions are given in terms of the feasibility of a convex optimization problem, involving linear matrix inequalities. A numerical example illustrates the applicability of the proposed technique. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a suitable model of distributed supply-chains (DSCs) is presented with the aim of providing a tool for DSC decentralised optimisation. To cope with this challenge, in the first part of the paper, a general model for distributed supply-chain including suppliers, processing units, assemblers, and transportation systems is presented with the aim of keeping the framework as general as possible. In the second part of the paper, an optimisation algorithm is also discussed. [Copyright &y& Elsevier]

Purpose - The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters - for newly considered serial-parallel and parallel-serial structures. Design/methodology/approach - The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested. Findings - The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters. Originality/value - The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge-based management-level decision systems for serial-parallel and parallel-serial structures of production systems. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters - for newly considered serial-parallel and parallel-serial structures. Design/methodology/approach - The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested. Findings - The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters. Originality/value - The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge-based management-level decision systems for serial-parallel and parallel-serial structures of production systems. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to develop and describe an improved process for determining the rate of heat generation in living tissue. Design/methodology/approach - Previous work by the authors on solving the bioheat equation has been updated to include a new localized meshless method which will create a more robust and computationally efficient technique. Inclusion of this technique will allow for the solution of more complex and realistic geometries, which are typical of living tissue. Additionally, the unknown heat generation rates are found through genetic algorithm optimization. Findings - The localized technique showed superior accuracy and significant savings in memory and processor time. The computational efficiency of the newly proposed meshless solver allows the optimization process to be carried to a higher level, leading to more accurate solutions for the inverse technique. Several example cases are presented to demonstrate these conclusions. Research limitations/implications - This work includes only 2D development of the approach, while any realistic modeling for patient-specific cases would be inherently 3D. The extension to 3D, as well as studies to improve the technique by decreasing the sensitivity to measurement noise and to incorporate non-invasive measurement positioning, are under way. Practical implications - As medical imaging continuously improves, such techniques may prove useful in patient diagonosis, as heat generation can be correlated to the presence of tumors, infections, or other conditions. Originality/value - This paper describes a new application of meshless methods. Such methods are becoming attractive due to their decreased pre-processing requirements, especially for problems involving complex geometries (such as patient specific tissues), as well as optimization problems, where geometries may be constantly changing. [ABSTRACT FROM AUTHOR]

This paper presents a constructive method for (sub)optimal finite-impulse response (FIR) approximation of infinite-impulse response (IIR) models. The method minimizes the Hankel norm of the approximation error by using an explicit solution to the norm-preserved dilation problem. It has advantages over the existing methods in that it is a unified method for both single-input single-output and multiple-input multiple-output systems which allows direct tradeoff between .the least-squares and Chebyshev error criteria by using a single tuning parameter, and that the approximation algorithm is constructive and only involves algebraic manipulations rather than iteration and convex optimization procedures. The lower and upper bounds on the l² and Chebyshev norms of the approximation error are derived and are related to the tuning parameter. The problem of approximating noncausal IIR models by causal FIR models is also formulated and solved. The effectiveness and properties of the proposed algorithms are demonstrated by examples. [ABSTRACT FROM AUTHOR]

This paper presents an adjoint-based algorithm for performing automatic parameter identification on differential equation models of biological systems. The algorithm locally solves an optimization problem, in which the cost reflects the deviation between the observed data and the output of the parameterized mathematical model, and the constraints are the governing parameterized equations. The tractability and the speed of convergence (to local minima) of the algorithm are strongly favorable to numerical parameter search algorithms which do not make use of the adjoint. Furthermore, initializing the algorithm with different instantiations of the parameters allows one to effectively search the parameter space. Results of the application of this algorithm to a previously presented mathematical model of planar cell polarity (PCP) signaling in the wings of Drosophila melanogaster are presented, and some new insights into the PCP mechanism that are enabled by the algorithm are described. [ABSTRACT FROM AUTHOR]

Abstract: This paper contains the basic extensions of classical evolutionary games (multipopulation and density dependent models). It is shown that classical bimatrix approach is inconsistent with other approaches because it does not depend on proportion between populations. The main conclusion is that interspecific proportion parameter is important and must be considered in multipopulation models. The paper provides a synthesis of both extensions (a metasimplex concept) which solves the problem intrinsic in the bimatrix model. It allows us to model interactions among any number of subpopulations including density dependence effects. We prove that all modern approaches to evolutionary games are closely related. All evolutionary models (except classical bimatrix approaches) can be reduced to a single population general model by a simple change of variables. Differences between classic bimatrix evolutionary games and a new model which is dependent on interspecific proportion are shown by examples. [Copyright &y& Elsevier]

When all training instances and labels are considered all together in a single optimization problem, multi-label support and core vector machines (i.e., Rank-SVM and Rank-CVM) are formulated as quadratic programming (QP) problems with equality and bounded constraints, whose training procedures have a sub-linear convergence rate. Therefore it is highly desirable to design and implement a novel efficient SVM-type multi-label algorithm. In this paper, through applying pairwise constraints between relevant and irrelevant labels, and defining an approximate ranking loss, we generalize binary Lagrangian support vector machine (LSVM) to construct its multi-label form (Rank-LSVM), resulting into a strictly convex QP problem with non-negative constraints only. Particularly, each training instance is associated with a block of variables and all variables are divided naturally into manageable blocks. Consequently we build an efficient training procedure for Rank-LSVM using random block coordinate descent method with a linear convergence rate. Moreover a heuristic strategy is applied to reduce the number of support vectors. Experimental results on twelve data sets demonstrate that our method works better according to five performance measures, and averagely runs 15 and 107 times faster and has 9 and 15% fewer support vectors, compared with Rank-CVM and Rank-SVM. [ABSTRACT FROM AUTHOR]

In the past decades, different evolutionary optimization methodologies have been proposed by scholars and exploited by practitioners, in a wide range of applications. Each paradigm shows distinctive features, typical advantages, and characteristic disadvantages; however, one single problem is shared by almost all of them: the “lack of speciation”. While natural selection favors variations toward greater divergence, in artificial evolution candidate solutions do homologize. Many authors argued that promoting diversity would be beneficial in evolutionary optimization processes, and that it could help avoiding premature convergence on sub-optimal solutions. The paper surveys the research in this area up to mid 2010s, it re-orders and re-interprets different methodologies into a single framework, and proposes a novel three-axis taxonomy. Its goal is to provide the reader with a unifying view of the many contributions in this important corpus, allowing comparisons and informed choices. Characteristics of the different techniques are discussed, and similarities are highlighted; practical ways to measure and promote diversity are also suggested. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven dynamic modeling method for the multivariate prediction of molten iron quality (MIQ) in a blast furnace (BF) using online sequential random vector functional-link networks (OS-RVFLNs) with the help of principal component analysis (PCA). At first, a data-driven PCA is employed to identify the most influential components from multitudinous factors that affect MIQ so as to reduce the model dimension. Secondly, a dynamic OS-RVFLNs modeling technology with fast learning and strong nonlinear mapping capability is proposed by applying the output self-feedback structure to the traditional OS-RVFLNs. Since it has been shown that such a dynamic modeling method has the ability to store and handle input-output data at different time scales, the dynamic OS-RVFLNs based MIQ prediction model has exhibited the potential for multivariable nonlinear mapping and the adaptability to dynamic time-varying process. Finally, some industrial experiments and comparative studies have been carried out on the 2# BF in Liuzhou Iron & Steel Group Co. of China using the proposed method, where it has been demonstrated that the constructed model produces a better modeling and estimating accuracy and has faster learning speed than other conventional MIQ modeling methods. [ABSTRACT FROM AUTHOR]

Users of web search engines are increasingly worried that their query activities may expose what topics they are interested in, and in turn, compromise their privacy. It would be desirable for a search engine to protect the true query intention for users without compromising the precision-recall performance. In this paper, we propose a client-based approach to address this problem. The basic idea is to issue plausible but innocuous pseudo queries together with a user query, so as to mask the user intention. First, we present a privacy model which formulates plausibility and innocuousness, and then the requirements which should be satisfied to ensure that the user intention is protected against a search engine effectively. Second, based on a semantic reference space derived from Wikipedia, we propose an approach to construct a group of pseudo queries that exhibit similar characteristic distribution as a given user query, but point to irrelevant topics, so as to meet the security requirements defined by the privacy model. Finally, we conduct extensive experimental evaluations to demonstrate the practicality and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Denoeux proposed an operator called the cautious conjunctive rule (CCR) to combine non-dogmatic belief functions (i.e., the frame is considered a focal element) from reliable dependent sources, which can occur in practice. However, in cases such as uncertain multiple attribute and group decision analyses, belief functions may denote data that vary in importance. This paper extends CCR as a weighted CCR (WCCR) to combine belief functions in consideration of their relative weights in such situations. Properties of WCCR are analyzed, proven, and demonstrated using numerical examples. In particular, the consideration of relative weights enables WCCR to combine dogmatic belief functions. The normalized WCCR is further constructed and used to assess the trustworthiness of a hospital information system employed in many hospitals in Anhui Province, China. [ABSTRACT FROM AUTHOR]

Unlike the widely existing H 2 / H ∞ filtering methods under single (i.e., wired or wireless) network with single channel environment, there exist multiple event-generators and different network-induced delays in hybrid wired–wireless networks, which make H 2 / H ∞ filtering analysis and design more complex. The main objective of this paper is to investigate multiple event-triggered H 2 / H ∞ filtering for hybrid wired–wireless networked systems with random network-induced delays. For multi-sensor network communications, a multiple event-triggered mechanism is employed firstly, which can reduce communication burden of each channel. The different communication characters of hybrid wired–wireless networks are then described by two Markov chains, and a general filtering error dynamic system model with the event-triggered parameters and multi-channel network-induced delays of hybrid wired–wireless networks is presented. Furthermore, the designed filter enables the filtering error dynamic system to be stochastic stability and to achieve a prescribed performance, and the relationships between the stability criteria and the maximum network-induced delays of hybrid wired–wireless networks, the event-triggered parameters and the system performance parameter are established. Finally, simulation results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Considering the practical application background in peer-to-peer network system, we investigate the single-variable term semi-latticized fuzzy relation geometric programming with max-product operator in this paper. Based on the characteristics of the objective function and feasible domain, we develop a matrix approach to deal with the proposed problem. A step-by-step algorithm is proposed to find an optimal solution without solving all the minimal solutions of the constraint. Two examples are given to illustrate the feasibility and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the notions of α -migrative uninorms over semi t-operators and α -migrative semi t-operators over uninorms are introduced and investigated. All solutions of the migrativity equations for all possible combinations of semi t-operators and uninorms are characterized. [ABSTRACT FROM AUTHOR]

Given a bounded lattice L with bounds 0 and 1, it is well known that the set Pol 0 , 1 ( L ) of all 0, 1-preserving polynomials of L forms a natural subclass of the set C ( L ) of aggregation functions on L . The main aim of this paper is to characterize all finite lattices L for which these two classes coincide, i.e. when the set C ( L ) is as small as possible. These lattices are shown to be completely determined by their tolerances, also several sufficient purely lattice-theoretical conditions are presented. In particular, all simple relatively complemented lattices or simple lattices for which the join (meet) of atoms (coatoms) is 1 (0) are of this kind. [ABSTRACT FROM AUTHOR]

In order to attain better reconstruction quality from compressive sensing (CS) of images, exploitation of the dependency or correlation patterns among the transform coefficients commonly has been employed. In this paper, we study a new image sensing technique, called compressive image sensing (CIS), with computational complexity O ( m 2 ), where m denotes the length of a measurement vector y = ϕ x , which is sampled from the signal x of length n via the sampling matrix ϕ with dimensionality m × n . CIS is basically a variation on compressive sampling. The contributions of CIS include: (i) reconstruction speed is extremely fast due to a closed-form solution being derived; (ii) certain reconstruction accuracy is preserved because significant components of x can be reconstructed with higher priority via an elaborately designed ϕ ; and (iii) in addition to conventional 1D sensing, we also study 2D separate sensing to enable simultaneous acquisition and compression of large-sized images. [ABSTRACT FROM AUTHOR]

The diversity of solutions is of great importance for multi-objective evolutionary algorithms. In this paper, a new multi-objective particle swarm optimization algorithm based on decomposition (MPSO/D) is proposed. Firstly, the objective space of a multi-objective problem is decomposed into a set of sub-regions based on a set of direction vectors. Then MPSO/D makes each sub-region have a solution to maintain the diversity. Secondly, considering the convergence of solutions, MPSO/D uses the crowding distance to calculate the fitness values of the reserved solutions for selection operator, and uses the neighboring particles of a particle to determine the global best historical position ( gbest ) of the particle. The proposed algorithm has been compared with NSGAII, MOEA/D and NNIA on sixteen test sets. The experimental results illustrate that the proposed algorithm outperforms NSGAII, MOEA/D and NNIA in terms of convergence and diversity. [ABSTRACT FROM AUTHOR]

Graph construction from data constitutes a pre-stage in many machine learning and computer vision tasks, like semi-supervised learning, manifold learning, and spectral clustering. The influence of graph construction procedures on learning tasks and their related applications has only received limited study despite its critical impact on accuracy. State-of-the-art graphs are built via sparse coding adopting ℓ 1 regularization. Those graphs exhibit good performance in many computer vision applications. However, the locality and similarity among instances are not explicitly used in the coding scheme. Furthermore, due to the use of ℓ 1 regularization, these construction approaches can be computationally expensive. In this paper, we investigate graph construction using the data self-representativeness property. By incorporating a variant of locality-constrained linear coding (LLC), we introduce and derive four variants for graph construction. These variants adopt a two phase LLC (TPLLC). Compared with the recent ℓ 1 graphs, our proposed objective function, associated with three variants, has an analytical solution, and thus, is more efficient. A key element of the proposed methods is the second phase of coding that allows data closeness, or locality, to be naturally incorporated. It performs a coding over some selected relevant samples and reinforces the individual regularization terms by exploiting the coefficients estimated in the first phase. Comprehensive experimental results using several benchmark datasets show that it can achieve or outperform existing state-of-the-art results. Furthermore, it is shown to be more efficient than the robust ℓ 1 graph construction schemes. [ABSTRACT FROM AUTHOR]

Nullnorms are generalization of triangular norms and triangular conorms with a zero element in the interior of the unit interval. In this paper, we generalize nullnorms to an arbitrary bounded lattices. We show the existence of nullnorms with zero element a for arbitrary element a ∈ L \{0, 1} using the fact that some t-norms and t-conorms on an arbitrary fixed bounded lattice L always exist. As a by-product we give a method of constructing nullnorms. Using this method, we obtain also the smallest nullnorm and the greatest nullnorm with given zero element a . Some other construction methods for nullnorms are also added. [ABSTRACT FROM AUTHOR]

To avoid traffic accidents, drivers must constantly be aware of nearby vehicles. Unfortunately, nearby vehicles often go unnoticed because of various obstacles such as other vehicles, buildings, or poor weather. In this paper, we study Moving range k -nearest neighbor (MR k NN) queries as a tool for continuously monitoring nearby moving objects. A simple approach to processing MR k NN queries is to have each object periodically broadcast information regarding its movements (i.e., location and velocity at a particular time) to other objects. However, this simple technique cannot be used to process MR k NN queries owing to the limited network bandwidth in mobile peer-to-peer environments. Therefore, we address this bandwidth limitation by proposing a probabilistic algorithm, called MINT, for M ov I ng range k - N N queries with quali T y guarantee over uncertain moving objects. MINT provides users with approximate answers with a quality guarantee, rather than exact answers, with near optimal communication costs. Using a series of simulations, we demonstrate the efficiency and efficacy of MINT in evaluating MR k NN queries with a quality guarantee. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on ranking values of polygonal fuzzy sets and automatically generated weights of fuzzy rules. First, the proposed method gets the characteristic points of the observation polygonal fuzzy sets and the characteristic points of the antecedent polygonal fuzzy sets of the fuzzy rules, respectively, by using the α-cut operations, respectively, where α ∈ [0, 1]. Then, it calculates the ranking value of each observation polygonal fuzzy set and the ranking value of each antecedent polygonal fuzzy set, respectively. Then, it calculates the difference between the ranking value of each observation polygonal fuzzy set and the ranking value of each antecedent polygonal fuzzy set. Then, it calculates the weight of each fuzzy rule based on the obtained differences between the ranking values of the observation polygonal fuzzy sets and the ranking values of the antecedent polygonal fuzzy sets. Then, based on the obtained characteristic points of the observation polygonal fuzzy sets, the obtained characteristic points of the antecedent polygonal fuzzy sets, and the obtained weights of the fuzzy rules, it gets the characteristic points of the fuzzy interpolative reasoning result represented by a polygonal fuzzy set. The experimental results show that the proposed method can overcome the drawbacks of the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. [ABSTRACT FROM AUTHOR]

Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing them from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that conceptualises the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users relationships are constantly evolving; and the information sharing norms are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks. In this paper, we propose the first computational model of Implicit Contextual Integrity, presenting an information model for Implicit Contextual Integrity as well as a so-called Information Assistant Agent that uses the information model to learn implicit contexts, relationships and the information sharing norms in order to help users avoid inappropriate information exchanges and undesired information disseminations. Through an experimental evaluation, we validate the properties of the model proposed. In particular, Information Assistant Agents are shown to: (i) infer the information sharing norms even if a small proportion of the users follow the norms and in presence of malicious users; (ii) help reduce the exchange of inappropriate information and the dissemination of sensitive information with only a partial view of the system and the information received and sent by their users; and (iii) minimise the burden to the users in terms of raising unnecessary alerts. [ABSTRACT FROM AUTHOR]