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Abstract: This paper deals with the problem of observer design for a class of implicit nonlinear systems of index one. To solve this problem, an implicit observer which is given by an implicit system is usually used. The implementation of this kind of observer requires an optimization algorithm to solve at each time the algebraic constraints. Alternatively, an explicit observer which is described by an ordinary differential equation can be used. The advantage of this approach is that no optimization algorithm is required. Moreover the observer initialization may be done outside the constraints. In this paper it is shown that, under some conditions, the existence of an implicit observer implies the existence of an explicit one. Then, an explicit observer is given for implicit state affine systems whose dynamic involves an additional nonlinear term depending on the input, the output and the implicit variable. [Copyright &y& Elsevier]

The method of absolute nodal coordinate formulation (ANCF) is used to solve the problem of determining the cable sags and the forces during the cable stringing and sagging process for overhead lines. The problem of calculating the sagging tables due to the temporary supporting cable sheave deflection during the construction phase is emphasized on hilly terrain. Two different models for the contact point between the cable and the sheave are analyzed. The most important advantage of this newly proposed approach is that it opens up the possibilities for analyzing more complex overhead-line structures that are a consequence of the line compaction, e.g., using unconventional insulating applications. The paper describes the basic steps of an ANCF algorithm to solve the presented problem. It also provides numerical examples in order to verify the ANCF method by comparing it with the results of other known approaches based on catenary equations. [ABSTRACT FROM AUTHOR]

Abstract: The linear complementarity problem is to find a vector in satisfying , ,, where and are given. In this paper, we use the fact that solving is equivalent to solving the nonlinear equation where is a function from into itself defined by . We build a sequence of smooth functions which is uniformly convergent to the function . We show that, an approximation of the solution of the (when it exists) is obtained by solving for a parameter large enough. Then we give a globally convergent hybrid algorithm which is based on vector divisions and the secant method for solving . We close our paper with some numerical simulations to illustrate our theoretical results, and to show that this method can solve efficiently large-scale linear complementarity problems. [Copyright &y& Elsevier]

Abstract: In this paper, we consider a class of stochastic linear complementarity problems (SLCPs) with finitely many elements. We present a smoothing Newton algorithm for solving the SLCP. Under suitable conditions, we obtain the global convergence and locally quadratic convergence of the proposed algorithm. Some numerical results are reported in this paper, which confirm the good theoretical properties of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper proposes a sequential approach to determine the unknown parameters for inverse heat conduction problems which have multiple time-dependent heat sources. There are two main aims in this study, one is to derive an inverse algorithm that can estimate the unknown conditions effectively, and the other is to bring up a theoretical sensitivity analysis to discuss what causes the growth of errors. This paper has three major achievements with regard to the literature on IHCPs, as follows: (1) proposing an efficient sequential inverse algorithm that can simultaneously determine several unknown time-dependent parameters; (2) exploring why the sequential function specification method can provide a stable but inaccurate estimation when tackling problems with larger measurement errors; and (3) discussing the sensitivity problem and analyzing what factors cause the growth in error sensitivity. Three examples are applied to demonstrate the performance of the proposed method, and the numerical results show that the accurate estimations can be obtained by alleviating the error sensitivity when the measurement error is considered. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the optimal locations of surveillance cameras on straight lanes. As known, inappropriate settings of surveillance cameras will result in some dead angles and dead space, and oversetting of surveillance cameras will waste limited resources. The considered location problem aims to minimize the maximal detection failure probability of points on straight lane subject to a limited budget. The problem is an important and complex location problem in practice. The purpose of this paper is twofold. Firstly, exact closed forms of optimal locations of single-type surveillance cameras are derived. Secondly, we apply a PSO algorithm for solving the types and locations of multiple-type surveillance cameras. Numerical results are reported and compared with those by well known software LINGO. Limited numerical results show the effectiveness of PSO especially when the budget is limited. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new fault prediction model is presented to deal with the fault prediction problems in the presence of both quantitative and qualitative data based on the dynamic evidential reasoning (DER) approach. In engineering practice, system performance is constantly changed with time. As such, there is a need to develop a supporting mechanism that can be used to conduct dynamic fusion with time, and establish a prediction model to trace and predict system performance. In this paper, a DER approach is first developed to realize dynamic fusion. The new approach takes account of time effect by introducing belief decaying factor, which reflects the nature that evidence credibility is decreasing over time. Theoretically, it is show that the new DER aggregation schemes also satisfy the synthesis theorems. Then a fault prediction model based on the DER approach is established and several optimization models are developed for locally training the DER prediction model. The main feature of these optimization models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune the DER prediction model whose initial parameters are decided by expert’s knowledge or common sense. Finally, two numerical examples are provided to illustrate the detailed implementation procedures of the proposed approach and demonstrate its potential applications in fault prediction. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies a class of bush-based algorithms (BA) for the user equilibrium (UE) traffic assignment problem, which promise to produce highly precise solutions by exploiting acyclicity of UE flows. Each of the two building blocks of BA, namely the construction of acyclic sub-networks (bush) and the solution of restricted master problems (RMP), is examined and further developed. Four Newton-type algorithms for solving RMP, which can be broadly categorized as route flow and origin flow based, are presented, of which one is newly developed in this paper. Similarities and differences between these algorithms, as well as the relevant implementation issues are discussed in great details. A comprehensive numerical study is conducted using both real and randomly generated networks, which reveals that the relative performance of the algorithms is consistent with the analysis. In particular, the results suggest that swapping flows between shortest and longest route segments consistently outperforms other RMP solution techniques. [Copyright &y& Elsevier]

Provided with the real spectrum, this paper presents a numerical procedure based on the induction principle to solve two types of inverse eigenvalue problems, one for nonnegative matrices and another for symmetric nonnegative matrices. As an immediate application, our approach can offer not only the sufficient condition to solve inverse eigenvalue problems for nonnegative or symmetric nonnegative matrices, but also a numerical way to solve inverse eigenvalue problems for stochastic matrices. Numerical examples are presented to show the capacity of our method. [ABSTRACT FROM AUTHOR]

In many phase-change problems of practical interest, it is important to know when a phase is depleted, a quantity referred to as the extinction time; however, there are no numerical schemes that are able to compute this with any degree of rigour or formal accuracy. In this paper, we develop such a scheme for the one-dimensional time-dependent problem of an evaporating spherical droplet. The Keller box finite-difference scheme is used, in tandem with the so-called boundary immobilization method. An important component of the work is the careful use of variable transformations that must be built into the numerical algorithm in order to preserve second-order accuracy in both time and space, in particular as regards resolving a square-root singularity in the droplet radius as the extinction time is approached. [ABSTRACT FROM AUTHOR]

In this paper, a new affine projection sign algorithm with a variable step-size, robust in impulsive noise environments, is proposed. For that purpose, ℓ1-norm of the a posteriori error vector is minimized under a box constraint on the step-size. Since the proposed ℓ1-norm minimization problem is non-differentiable convex one, an efficient numerical procedure is developed for the affine projection structure algorithm. Finally, it is demonstrated that the proposed algorithm, not requiring any a priori knowledge, yields more robust convergence in various impulsive noise environments than conventional algorithms with robustness to outliers and recent variable step-size algorithms with a priori information. [ABSTRACT FROM AUTHOR]

Abstract: We propose two new algorithms to minimize the constant modulus (CM) criterion in the context of blind source separation. The first algorithm, referred to as Givens CMA (G-CMA), uses unitary Givens rotations and proceeds in two stages: prewhitening step, which reduces the channel matrix to a unitary one followed by a separation step where the resulting unitary matrix is computed as a product of Givens rotations. However, for small sample sizes, the prewhitening does not make the channel matrix close enough to unitary and hence applying Givens rotations alone does not provide satisfactory performance. To remediate to this problem, we propose to use Hyperbolic rotations in conjunction with Givens rotations. This second algorithm, referred to as Hyperbolic G-CMA (HG-CMA), is shown to outperform the G-CMA as well as the Analytical CMA (ACMA). The last part of this paper is dedicated to an efficient adaptive implementation of the HG-CMA and to performance assessment through numerical experiments. [Copyright &y& Elsevier]

Fast algorithms for solving symmetric penta-diagonal Toeplitz linear systems have been proposed in McNally (2010) [7], Nemani (2010) [8] and Xu (2010) [11]. In this paper, we discuss the general nonsymmetric problem and propose an algorithm for solving nonsymmetric penta-diagonal Toeplitz linear systems. Numerical examples are given to illustrate the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Microaggregation is a successful mechanism to solve the tension between respondent privacy and data quality in the context of Statistical Disclosure Control. Microaggregation, for numerical datasets, is defined as a clustering problem with the constraint of having at least k records in each group, such that the sum of the within-group squared error (SSE) is minimized. Unfortunately, the data publisher has to execute an algorithm iteratively for different values of k to investigate a good trade-off between privacy and utility. Multiple execution of an algorithm on large numerical datasets is resource wasting, since most of the computations are repetitive. In this paper, we propose a Fast Data-oriented Microaggregation algorithm (FDM) that efficiently anonymizes large multivariate numerical datasets for multiple successive values of k. Experimental results on real world datasets demonstrate the superiority of the method in terms of both the data quality and time complexity. Moreover, the method usually achieves a better trade-off between disclosure risk and information loss of the protected dataset in comparison with previous techniques. [ABSTRACT FROM AUTHOR]

Abstract: The normalized least mean squares (NLMS) and recursive least squares (RLS) algorithms are widely used for adaptive filtering. Interestingly, the NLMS algorithm has been shown to be strictly optimal in the sense of filtering, whereas the forgetting factor RLS algorithm has not been clearly related to a solution to the filtering problem. This paper describes a method for further optimizing the solutions to the ordinary filtering problem over an assumed system model set and a predetermined norm weight set. The extended filtering problem offers a framework for constructing a unified view of adaptive algorithms for finite impulse response (FIR) filters. The framework enables a discussion of the relationships among the NLMS algorithm, the forgetting factor RLS algorithm, and the filter over the common parameter space, and facilitates the development of new fast adaptive algorithms that outperform the existing algorithms, such as the NLMS and the fast RLS algorithms. The validity of the discussion based on the framework is verified using numerical examples. [Copyright &y& Elsevier]

Abstract: Recently, it has been found that the redundant blocks problem existed in many fields, such as face recognition and motion segmentation. In this paper, taking the redundant blocks into account, we propose some greedy type algorithms that exploit the subspace information of the redundant blocks to solve the redundant blocks problem. The exact recovery conditions of these algorithms are presented via block restricted isometry property (RIP). Numerical experiments demonstrate the validity of these algorithms in solving the problems with both non-redundant and redundant blocks. [Copyright &y& Elsevier]

Abstract: In this paper, we study the real eigenvalue bounds of standard and generalized real interval eigenvalue problems. Based on some known sufficient conditions for the regularity of interval matrices, a new algorithm for computing real eigenvalue bounds for real interval matrices is proposed. It can easily be extended for applying to generalized real interval eigenvalue problems. One advantage of the method is that the computation procedure takes less time than former methods we have proposed. Therefore it can be applied for solving larger interval eigenvalue problems. Finally, some numerical examples are presented which show the effectiveness of the method. [Copyright &y& Elsevier]

Abstract: In this paper, the generalized complementarity problem is studied. Based on a new smoothing function, the generalized complementarity problem is solved by a smoothing Newton-type algorithm. Under suitable conditions, we prove that the iteration sequence generated by the proposed smoothing method is bounded and the proposed algorithm is globally convergent. Some numerical results are reported. [Copyright &y& Elsevier]

Abstract: Various types of partial differential equations have been playing increasingly important roles in the study of theoretical and numerical combustion. In this paper, we are particularly concerned with the numerical solution of certain premixed model problems in rectangular spatial domains. The two-dimensional reaction–diffusion equations involved are associated with an ignition type nonlinearity involving a mathematical degeneracy at a corner point. A Peaceman–Rachford–Strang splitting based adaptive method is proposed on exponentially evolving grids. Rigorous numerical analysis are given to ensure the satisfactory effectiveness, efficiency, and numerical stability of the algorithm developed. Simulation experiments are provided to illustrate our accomplishments. [Copyright &y& Elsevier]

Abstract: In this paper we present the implementation and the application of non-intrusive spectral techniques for uncertainty analysis of criticality problems. Spectral techniques can be used to reconstruct stochastic quantities of interest by means of a Fourier-like expansion. Their application to uncertainty propagation problems can be performed in a non-intrusive fashion by evaluating a set of projection integrals that are used to reconstruct the spectral expansion. This can be done either by using standard Monte Carlo integration approaches or by adopting numerical quadrature rules. We present the derivation of a new adaptive quadrature algorithm, based on the definition of a sparse grid, which can be used to reduce the computational cost associated with non-intrusive spectral techniques. This new adaptive algorithm and the Monte Carlo integration alternative are then applied to two reference problems. First, a stochastic multigroup diffusion problem is introduced by considering the microscopic cross-sections of the system to be random quantities. Then a criticality benchmark is defined for which a set of resonance parameters in the resolved region are assumed to be stochastic. [Copyright &y& Elsevier]

Abstract: The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model. [Copyright &y& Elsevier]

Abstract: The split feasibility problem models inverse problems arising from phase retrieval problems and the intensity modulated radiation therapy. In this paper, two methods have been presented for solving the split feasibility problem. The strong convergence results of presented algorithms have been obtained under some mild conditions. Especially, the minimum norm solution of the split feasibility problem can be found. [Copyright &y& Elsevier]

Abstract: In this paper, a trust-region sequential quadratic programming algorithm with a modified filter acceptance mechanism is proposed for nonlinear equality constrained optimization. The most important advantage of the proposed algorithm is its avoidance of any feasibility restoration phase, a necessity in traditional filter methods. We solve quadratic programming subproblems based on the well-known Byrd–Omojokun trust-region method. Inexact solutions to these subproblems are allowed. Under some standard assumptions, global convergence of the proposed algorithm is established. Numerical results show our approach is potentially useful. [Copyright &y& Elsevier]

Abstract: As an important managerial problem, the practical joint replenishment and delivery (JRD) model under stochastic demand can be regarded as the combination of a joint replenishment problem and traveling salesman problem, either one is an NP-hard problem. However, due to the JRD’s difficult mathematical properties, high quality solutions for the problem have eluded researchers. This paper firstly proposes an effective and efficient hybrid differential evolution algorithm (HDE) based on the differential evolution algorithm (DE) and genetic algorithm (GA) that can solve this NP-hard problem in a robust and precise way. After determining the appropriate parameters of the HDE by parameters tuning test, the effectiveness and efficiency of the HDE are verified by benchmark functions and numerical examples. We compare the HDE with the available best approach and find that the HDE can always obtain the slightly lower total costs under some situations. Compared with another popular evolutionary algorithm, results of numerical examples also show HDE is faster than GA and the convergence rate of HDE is higher than GA. HDE is a strong candidate for the JRD under stochastic demand. [Copyright &y& Elsevier]

Abstract: Model updating should be correlated with experimental data to ensure that it models the dynamics of the real structure and the updated model predicts the dynamics of a structure more accurately. Considering that the iterative methods for model updating have aroused little public attention, this paper studies an iterative algorithm for quadratic model updating problems which can incorporate the measured modal data into the finite element model to produce an adjusted finite element model on the mass, gyroscopic and stiffness matrices that closely match the experimental modal data. By this method, the best approximation symmetric and skew-symmetric solutions can be obtained by choosing a convergence factor. Numerical example shows that the introduced iterative algorithm is quite efficient. [Copyright &y& Elsevier]

Abstract: Quadratic knapsack problem has a central role in integer and nonlinear optimization, which has been intensively studied due to its immediate applications in many fields and theoretical reasons. Although quadratic knapsack problem can be solved using traditional nonlinear optimization methods, specialized algorithms are much faster and more reliable than the nonlinear programming solvers. In this paper, we study a mixed linear and quadratic knapsack with a convex separable objective function subject to a single linear constraint and box constraints. We investigate the structural properties of the studied problem, and develop a simple method for solving the continuous version of the problem based on bi-section search, and then we present heuristics for solving the integer version of the problem. Numerical experiments are conducted to show the effectiveness of the proposed solution methods by comparing our methods with some state of the art linear and quadratic convex solvers. [Copyright &y& Elsevier]

Abstract: In this paper, a new numerical approach via reconstructed Legendre orthogonal polynomials (LOPs) is presented to solve the linear quadratic optimal control problems (LQPs). By using the elegant operational properties of orthogonal polynomials, a computationally attractive algorithm is developed for calculating LQP. A numerical example illustrates the techniques and demonstrates the accuracy and efficiency of these controllers. [Copyright &y& Elsevier]

Abstract: The vehicle navigation problem studied in is revisited and a time-dependent reverse Hyperstar algorithm is presented. This minimises the expected time of arrival at the destination, and all intermediate nodes, where expectation is based on a pessimistic (or risk-averse) view of unknown link delays. This may also be regarded as a hyperpath version of the algorithm, which itself is a time-dependent A * algorithm. Links are assigned undelayed travel times and maximum delays, both of which are potentially functions of the time of arrival at the respective link. Probabilities for link use are sought that minimise the driver’s maximum exposure to delay on the approach to each node, leading to the determination of a pessimistic expected time of arrival at the destination and all intermediate nodes. Since the context considered is vehicle navigation, the probability of link use measures link attractiveness, so a link with a zero probability of use is unattractive while a link with a probability of use equal to one will have no attractive alternatives. A solution algorithm is presented and proven to solve the problem provided the node potentials are feasible and a FIFO condition applies to undelayed link travel times. The paper concludes with a numerical example. [Copyright &y& Elsevier]

Abstract: In this paper, an algorithm is presented for solving second-order nonlinear multi-point boundary value problems (BVPs). The method is based on an iterative technique and the reproducing kernel method (RKM). Two numerical examples are provided to show the reliability and efficiency of the present method. [Copyright &y& Elsevier]

Abstract: The quayside operation problem is one of the key components in the management system for a container terminal. In this paper, the integrated models proposed in the previous studies to address the quayside operation problem are examined and one of the potential frameworks is identified. A new method called combinatorial benders’ cuts algorithm is developed to solve the berth-level model in the framework. The computational experiment conducted in this research shows that the proposed approach is more efficient than the branch and cut algorithm embedded in CPLEX. [Copyright &y& Elsevier]

Abstract: A DoS attack can be regarded as an attempt of attackers to prevent legal users from gaining a normal network service. The TCP connection management protocol sets a position for a classic DoS attack, namely, the SYN flood attack. In this attack some sources send a large number of TCP SYN segments, without completing the third handshake step to quickly exhaust connection resources of the under attack system. This paper models the under attack server by using the queuing theory in which attack requests are recognized based on their long service time. Then it proposes a framework in which the defense issue is formulated as an optimization problem and employs the particle swarm optimization (PSO) algorithm to optimally solve this problem. PSO tries to direct the server to an optimum defense point by dynamically setting two TCP parameters, namely, maximum number of connections and maximum duration of a half-open connection. The simulation results show that the proposed defense strategy improves the performance of the under attack system in terms of rejection probability of connection requests and efficient consumption of buffer space. [Copyright &y& Elsevier]

Abstract: The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail. [Copyright &y& Elsevier]

Abstract: In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. [Copyright &y& Elsevier]

Abstract: In this paper, we present a generic mixed-integer linear multistage stochastic programming (MSSP) model considering endogenous uncertainty in some of the parameters. To address the issue that the number of non-anticipativity (NA) constraints increases exponentially with the number of uncertain parameters and/or its realizations, we present a new theoretical property that significantly reduces the problem size and complements two previous properties. Since one might generate reduced models that are still too large to be solved directly, we also propose three solution strategies: a k-stage constraint strategy where we only include the NA constraints up to a specified number of stages, an iterative NAC relaxation strategy, and a Lagrangean decomposition algorithm that decomposes the problem into scenarios. Numerical results for two process network examples are presented to illustrate that the proposed solution strategies yield significant computational savings. [Copyright &y& Elsevier]

Abstract: Highly reliable systems can reduce loss of money and time in practice. System reliability can be enhanced by: (i) increasing component reliabilities and/or (ii) providing redundancy at the component level. A trade-off between these two options is necessary for nonlinear-constrained reliability optimization. The problem of maximizing system reliability through component reliability choices and component redundancy is called as reliability–redundancy allocation problem, and it is a difficult but realistic nonlinear mixed-integer optimization problem. In this paper, under nonlinear constraints of weight, cost, and volume, we propose a new immune based two-phase approach to solve the reliability–redundancy allocation problem. In the first phase, an immune based algorithm (IA) is developed to solve the allocation problem, and in the second phase we present a new procedure to improve the solutions by IA. Numerical results of four benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature. [Copyright &y& Elsevier]

Abstract: This paper deals with the super-resolution (SR) problem based on a single low-resolution (LR) image. Inspired by the local tangent space alignment algorithm in for nonlinear dimensionality reduction of manifolds, we propose a novel patch-learning method using locally affine patch mapping (LAPM) to solve the SR problem. This approach maps the patch manifold of low-resolution image to the patch manifold of the corresponding high-resolution (HR) image. This patch mapping is learned by a training set of pairs of LR/HR images, utilizing the affine equivalence between the local low-dimensional coordinates of the two manifolds. The latent HR image of the input (an LR image) is estimated by the HR patches which are generated by the proposed patch mapping on the LR patches of the input. We also give a simple analysis of the reconstruction errors of the algorithm LAPM. Furthermore we propose a global refinement technique to improve the estimated HR image. Numerical results are given to show the efficiency of our proposed methods by comparing these methods with other existing algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we analyze a FEM and two-grid FEM decoupling algorithms for elliptic problems on disjoint domains. First, we study the rate of convergence of the FEM and, in particular, we obtain a superconvergence result. Then with proposed algorithms, the solution of the multi-component domain problem (simple example — two disjoint rectangles) on a fine grid is reduced to the solution of the original problem on a much coarser grid together with solution of several problems (each on a single-component domain) on fine meshes. The advantage is the computational cost although the resulting solution still achieves asymptotically optimal accuracy. Numerical experiments demonstrate the efficiency of the algorithms. [Copyright &y& Elsevier]

Abstract: We study an on-line machine covering problem, in which jobs arrive one by one and their processing times are known upon their arrival, and jobs are allowed to migrate between machines when a new job is added in the system. However, the total processing time of migration induced by an incoming job is bounded by a constant factor times the processing time of the incoming job. The objective is to maximize the minimum machine load. In this paper, we present an on-line algorithm with competitive ratio 6/5 for the two identical machines case with . Moreover, the presented on-line algorithm is only a local migration, that is, when one job is assigned to machine , only the jobs on machine are allowed to migrate. We also show that the provided algorithm is a best possible on-line algorithm in the sense of local migration. [Copyright &y& Elsevier]

Abstract: This paper presents a two-grid method for solving systems of partial differential equations modelling incompressible free flow coupled with porous media flow. This work considers both the coupled Stokes and Darcy as well as the coupled Navier–Stokes and Darcy problems. The numerical schemes proposed are based on combinations of the continuous finite element method and the discontinuous Galerkin method. Numerical errors and convergence rates for solutions obtained from the two-grid method are presented. CPU times for the two-grid algorithm are shown to be significantly less than those obtained by solving the fully coupled problem. [Copyright &y& Elsevier]

Abstract: This paper deals with an inverse problem of determining a source term in the one-dimensional fractional advection–dispersion equation (FADE) with a Dirichlet boundary condition on a finite domain, using final observations. On the basis of the shifted Grünwald formula, a finite difference scheme for the forward problem of the FADE is given, by means of which the source magnitude depending upon the space variable is reconstructed numerically by applying an optimal perturbation regularization algorithm. Numerical inversions with noisy data are carried out for the unknowns taking three functional forms: polynomials, trigonometric functions and index functions. The reconstruction results show that the inversion algorithm is efficient for the inverse problem of determining source terms in a FADE, and the algorithm is also stable for additional data having random noises. [Copyright &y& Elsevier]

Abstract: Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The introduced catfish particles improve the performance of PSO considerably. Unlike other ordinary particles, the catfish particles initialize a new search from extreme points of the search space when the gbest fitness value (global optimum at each iteration) has not changed for a certain number of consecutive iterations. This results in further opportunities of finding better solutions for the swarm by guiding the entire swarm to promising new regions of the search space and accelerating the search. The introduced chaotic maps strengthen the solution quality of PSO and CatfishPSO significantly. The resulting improved PSO and CatfishPSO are called chaotic PSO (C-PSO) and chaotic CatfishPSO (C-CatfishPSO), respectively. PSO, C-PSO, CatfishPSO, C-CatfishPSO, as well as other advanced PSO procedures from the literature were extensively compared on several benchmark test functions. Statistical analysis of the experimental results indicate that the performance of C-CatfishPSO is better than the performance of PSO, C-PSO, CatfishPSO and that C-CatfishPSO is also superior to advanced PSO methods from the literature. [Copyright &y& Elsevier]

Abstract: Adaptability is the most attractive feature of the smoothed particle hydrodynamics (SPH) method. Therefore it can naturally handle problems with extremely large deformation. Fluid–structure interaction (FSI) problems are placed in this category. In FSI problems, using a good contact algorithm is necessary to achieve fidelity of the results. This paper presents a new numerical simulation based on the SPH method for FSI problems. The main contribution of this work is proposing new algorithms to model the contact between a viscous fluid and hypo-elastic solid particles. In the proposed contact model, the interpenetrations of fluid and solid particles are prevented by introducing a repulsive force and new intermediate particles. The validity of the proposed algorithms is verified, and in order to show their ability several examples are solved. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an interval version of the generalized accelerated overrelaxation methods, which we refer to as IGAOR, for solving the linear complementarity problems, LCP (M, q), and develop a class of multisplitting IGAOR methods which can be easily implemented in parallel. In addition, in regards to the H-matrix with positive diagonal elements, we prove the convergence of these algorithms and illustrate their efficiency through our numerical results. [Copyright &y& Elsevier]

Abstract: Many real life problems can be stated as a minimax optimization problem, such as the problems in economics, finance, management, engineering and other fields. In this paper, we present an algorithm with nonmonotone strategy and second-order correction technique for minimax optimization problems. Using this scheme, the new algorithm can overcome the difficulties of the Maratos effect occurred in the nonsmooth optimization, and the global and superlinear convergence of the algorithm can be achieved accordingly. Numerical experiments indicate some advantages of this scheme. [Copyright &y& Elsevier]

Abstract: This paper deals with a recently proposed algorithm for obtaining all weak efficient and efficient solutions in a multi objective linear programming (MOLP) problem. The algorithm is based on solving some weighted sum problems, and presents an easy and clear solution structure. We first present an example to show that the algorithm may fail when at least one of these weighted sum problems has not a finite optimal solution. Then, the algorithm is modified to overcome this problem. The modified algorithm determines whether an efficient solution exists for a given MOLP and generates the solution set correctly (if exists) without any change in the complexity. [Copyright &y& Elsevier]

Abstract: This paper revisits the problem of infinite horizon constrained linear quadratic regulation (LQR) for discrete-time systems. It is known that there exists a finite horizon such that the infinite horizon constrained LQR problem can be solved as a finite horizon constrained LQR problem. We first propose several algorithms to estimate the upper bound on the length of this finite horizon. Conservativeness and computational complexity of these algorithms are compared through numerical examples. [Copyright &y& Elsevier]

Abstract: This paper proves the convergence of some generalized Schwarz algorithms for solvingthe obstacle problems with a T-monotone operator. Numerical results show that the generalized Schwarz algorithms converge faster than the classical Schwarz algorithms. [Copyright &y& Elsevier]