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This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Highlights • Dynamical analysis has been provided to describe nonlinear behavior of supercavitating vehicles. • Robust H∞ loop-shaping synthesis with modified PID algorithm is proposed to control the dive plane maneuver of the HSSV. • Multi-objective control problems are solved using BMI optimization of an equivalent Schur formula. • Control scheme has the low order structure and provides robustness against uncertainties. • Integrated robust controller can deal with high parametric uncertainties and suppress exogenous disturbances and sensor noises. Abstract The high-speed supercavitating vehicle (HSSV) utilizes advanced technology that enables an underwater vehicle to reach its unprecedented high speed. The vertical motion control of the HSSV is challenging problem because of its complex dynamics with nonlinear planing force, parametric uncertainties, external disturbances, actuator saturation, and sensor noises. This paper deals with dynamical analysis and a robust H∞ loop-shaping synthesis with modified PID (proportional-integral-derivative) algorithm to control the dive plane maneuver of the HSSV. Typically, the control scheme has the low order structure and provides robustness against dynamic uncertainties, which can be implemented using the bilinear matrix inequality (BMI) optimization of an equivalent Schur formula. Simulation results show that the controlled vehicle system provides good performance and high robustness against uncertainties, ensuring no-overshoot and fast in time-domain responses. In addition, the control algorithm can decouple the dynamic interactions in the multi-input multi-output (MIMO) system, overcoming parametric uncertainty, planing force, and actuator saturation while minimizing the effect of the strong external disturbances and measurement noises. [ABSTRACT FROM AUTHOR]

In this paper, we introduce obnoxious effects into obnoxious facility location games on a line, where each agent i has a private location x i on a closed interval [ 0 , 1 ] and one facility will be built on a location y in the interval according to the bids of all the agents. In addition, there are two thresholds d 1 and d 2 in the utility function of each agent, where 0 ≤ d 1 ≤ d 2 ≤ 1 . Denote d ( y , x i ) = | y − x i | to be the distance between agent i and the facility on the location y . The utility function of agent i is 0 if d ( y , x i ) is at most d 1 ; 1 if d ( y , x i ) is at least d 2 ; otherwise a linear increasing function between 0 and 1. Each agent attempts to get the largest utility while the social welfare is to maximize the sum of all the agents' utilities. The classic obnoxious facility game [4,11] is a special case of our problem when d 1 = 0 and d 2 = 1 . In this work, we first study the hardness of approximate mechanism design on this generalized problem, which states that our problem cannot admit any deterministic strategy-proof mechanism with bounded approximation ratio if d 1 ≥ 1 2 . Then we limit the thresholds to some ranges, both deterministic and randomized strategy-proof mechanisms are studied, and the approximation ratios vary with the specific values of d 1 and d 2 . [ABSTRACT FROM AUTHOR]

The global solvers are an attractive class of iterative solvers for solving linear systems with multiple right-hand sides. In this paper, first, a new global method for solving general linear systems with several right-hand sides is presented. This method is the global version of the LSMR algorithm presented by Fong and Saunders (2011). Then, some theoretical properties of the new method are discussed. Finally, numerical experiments from real applications are used to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: This paper is to illustrate that the main result of the paper [R.U. Verma, Generalized over-relaxed proximal algorithm based on -maximal monotonicity framework and applications to inclusion problems, Mathematical and Computer Modelling 49 (2009) 1587–1594] is incorrect. The convergence rate of the over-relaxed proximal point algorithm should be greater than 1. Moreover, the strong convergence and the unique solution may not be proved accordingly in the paper by Verma. [Copyright &y& Elsevier]

Abstract: The mesh density has an important affect on finite element analysis of engineering problems. This paper studies the adaptive and local refinement techniques of hexahedral element meshes. A set of refinement templates and its converting rules of refinement fields are established. The refinement field propagation problem is solved using two corner templates and an isolated node template. A relative curvature criterion for constructing refinement source point fields is proposed by introducing the relative area of STL (stereo lithography) facets into the curvature criterion. The adaptive refinement of meshes can be easily realized using the constructed refinement source point fields. Examples show that the relative curvature criterion can more accurately capture the curvature features of solid models than the conventional curvature refinement criterion. To facilitate users and obtain the effective local refinement, this paper proposes a local refinement technique. The levels of local refinement can be self-controlled by users. The geometric features can be easily identified before each level of refinement. The effective refinement of nodes, elements, element-edges, element-surfaces, mesh-boundaries, mesh-faces and local regions can be realized. The accuracy and robustness of the adaptive and local refinement algorithms presented in this paper are demonstrated using several examples. The mesh refinement quality and flexibility are improved significantly. [Copyright &y& Elsevier]

Abstract: The focus of this paper is to propose a hybrid neural network model for associative recall of analog and digital patterns. This hybrid model consists of self-feedback neural network structures (SFNN) in parallel with generalized regression neural networks (GRNN). Using a new one-shot learning algorithm developed in the paper, pattern representations are first stored as the asymptotically stable fixed points of the SFNN. Then in the retrieving process, each pattern is applied to the GRNN to make the corresponding initial condition and to initiate the dynamical equations of the SFNN that should in turn output the corresponding representation. In this way, the corresponding stored patterns are retrieved even under high noise degradation. Moreover, contrary to many associative memories, the proposed hybrid model is without any spurious attractors and can store both binary and real-value patterns without any preprocessing. Several simulations confirm the theoretical analyses of the model. Results indicate that the performance of the hybrid model is better than that of recurrent associative memory and competitive with other classes of networks. [Copyright &y& Elsevier]

Abstract: This paper describes non-manifold offsetting operations that add or remove a uniform thickness from a given non-manifold topological model. The mathematical definitions and properties of the non-manifold offsetting operations are investigated first, and then an offset algorithm based on the definitions is proposed and implemented using the non-manifold Euler operators proposed in this paper. In this algorithm, the offset elements of minimal size for the vertices, edges and faces of a given non-manifold model are generated first. Then, they are united into a single body using the non-manifold Boolean operations. In order to reduce computation time and numerical errors, the intersections between the offset elements are calculated considering the origins of the topological entities during union. Finally, all topological entities that are within the offset distance are detected and removed in turn. In addition to the original offset algorithm based on mathematical definitions, some variant offset algorithms, called sheet thickening and solid shelling, are proposed and implemented for more practical and efficient solid modeling of thin-walled plastic or sheet metal parts. In virtue of the proposed non-manifold offset operation and its variations, different offsetting operations for wireframes, sheets and solids can be integrated into one and applied to a wide range of applications with a great potential usefulness. [Copyright &y& Elsevier]

Abstract: Threshold group testing first proposed by Damaschke is a generalization of classic group testing. Specifically, a group test is positive (negative) if it contains at least (at most ) positives, and if the number of positives is between and , the test outcome is arbitrary. Although sequential group testing algorithms have been proposed, it is unknown whether an efficient nonadaptive algorithm exists. In this paper, we give an affirmative answer to this problem by providing efficient nonadaptive algorithms for the threshold model. The key observation is that disjunct matrices, a standard tool for group testing designs, also work in this threshold model. This paper improves and extends previous results in three ways: 1. The algorithms we propose work in one stage, which saves time for testing. 2. The test complexity is lower than previous results, at least for the number of elements which need to be tested is sufficiently large. 3. A limited number of erroneous test outcomes are allowed. [Copyright &y& Elsevier]

Abstract: The effectiveness of the first entry of the original series by GM(1, 1) is researched in this paper. The results show that the modelling values and forecasts are independent of the first entry of the original series. The grey prediction model presented in this paper is called first-entry GM(1, 1), abbreviated as FGM(1, 1), which is based on the existing GM(1, 1) but modelled with data including the first-entry’s messages of the original series. A proof concerning this subject has been presented by other authors. However, the algorithm of their direct proof is too complicated. A more compact algorithm is presented in this paper to prove the first entry of the original series ineffective to the modelling values and forecasts by GM(1, 1). Then, an arbitrary number can be inserted in the front of the original series to extract the messages from its first entry. Only a few data (usually fewer than ten) are used for model building. This paper deals with the effectiveness of the first entry of the original series by GM(1, 1). [Copyright &y& Elsevier]

A BC-tree (block-cutpoint-tree) is a tree (with at least two vertices) where the distance between any two leaves is even. A BC-subtree is a subtree of a connected graph that is also a BC-tree. In this paper, we first consider subtrees containing a specific vertex with conditions on the parity of the distances from this vertex to the leaves and the enumeration of such subtrees of unicyclic and edge-disjoint bicyclic graphs. Using these results and through generating functions of BC-subtrees, we present graph-theoretical based algorithms for enumerating BC-subtrees, BC-subtrees containing a given vertex, and BC-subtrees containing two distinct vertices of unicyclic and edge-disjoint bicyclic graphs. These results also provide a novel perspective on exploring the structural properties of molecules. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel unsupervised feature selection algorithm, named sparse preserving feature weights learning (SPFW), which is based on the recent local data representation theory, sparse representation. SPFW differs from traditional feature selection algorithms in two aspects: (1) SPFW is designed on the locality measurement criterion with sparse reconstruction residual minimization. It adaptively determines the locality based on sparse representation, instead of fixing the k -nearest neighbors in the original feature space. (2) SPFW selects the most discriminative feature subset from the whole feature set in batch mode, instead of selecting features individually. To optimize the proposed formulation, we propose an efficient iterative algorithm, where each iteration reduces to a subproblem which can be solved with some off-the-shelf toolboxes. We conduct experiments on two face datasets to evaluate the performance of feature selection in terms of classification and clustering, which demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) are one of the most popular and powerful machine learning techniques, but suffer from a significant drawback of the high time and memory complexities of their training. This issue needs to be endured especially in the case of large and noisy datasets. In this paper, we propose a new adaptive memetic algorithm (PCA 2 MA) for selecting valuable SVM training data from the entire set. It helps improve the classifier score, and speeds up the classification process by decreasing the number of support vectors. In PCA 2 MA, a population of reduced training sets undergoes the evolution, which is complemented by the refinement procedures. We propose to exploit both a priori information about the training set—extracted using the data geometry analysis—and the knowledge attained dynamically during the PCA 2 MA execution to enhance the refined sets. Also, we introduce a new adaptation scheme to control the pivotal algorithm parameters on the fly, based on the current search state. Extensive experimental study performed on benchmark, real-world, and artificial datasets clearly confirms the efficacy and convergence capabilities of the proposed approach. We demonstrate that PCA 2 MA is highly competitive compared with other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Learning-based partial differential equations (PDEs), which combine fundamental differential invariants into a nonlinear regressor, have been successfully applied to several computer vision and image processing problems. However, the gradient descent method (GDM) for solving the linear combination coefficients among differential invariants is time-consuming. Moreover, when the regularization or constraints on the coefficients become more complex, it is troublesome or even impossible to deduce the gradients. In this paper, we propose a new algorithm, called fast alternating time-splitting approach (FATSA), to solve the linear combination coefficients. By minimizing the difference between the expected output and the actual output of PDEs at each time step , FATSA can solve the linear combination coefficients much faster than GDM. More complex regularization or constraints can also be easily incorporated. Extensive experiments demonstrate that our proposed FATSA outperform GDM in both speed and quality. [ABSTRACT FROM AUTHOR]

This paper deals with the reliable H ∞ - based filtering problem of nonlinear networked control systems in the presence of external disturbances and random sensor faults. More precisely, a random variable obeying the Bernoulli distribution is adopted to describe the failures of the network sensors. By employing the Lyapunov–Krasovskii functional method, delay-dependent criteria are developed to guarantee the existence of the designed filter with the prescribed H ∞ performance. Then, a design method is given to calculate the desired reliable filter parameters. An illustrative example is provided to demonstrate the effectiveness and usefulness of the established theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the quantized distributed optimization problem over general directed digital multi-agent networks, where the communication channels have limited data transmission rates. To solve the optimization problem, a distributed quantized subgradient algorithm is presented among agents. Based on an encoder–decoder scheme and a zoom-in technique, we can achieve not only a consensus, but also an optimal solution. In particular, we study two cases of the quantization levels of each connected directed digital communication channel. One is under the case that the quantization levels are time-varying at each time step, and the other is under the case of fixed quantization level. Two rigorous theoretical analyses are performed and the optimal solutions can be obtained asymptotically. Moreover, the upper bound of the quantization levels at each time step and the convergence rate are analytically characterized. The effectiveness of proposed algorithm is demonstrated by two illustrative examples. [ABSTRACT FROM AUTHOR]

The use of degradation data to estimate the remaining useful life (RUL) has gained great attention with the widespread use of prognostics and health management on safety critical systems. Accurate RUL estimation can prevent system failure and reduce the running risks since the efficient maintenance service could be scheduled in advance. In this paper, we present a degradation modeling and RUL estimation approach by using available degradation data for a deteriorating system. An inverse Gaussian process with the random effect is firstly used to characterize the degradation process of the system. Expectation maximization algorithm is then adopted to estimate the model parameters, and the random parameters in the degradation model are updated by Bayesian method, which makes the estimated RUL able to be real-time updated in terms of the fresh degradation data. Our proposed method can capture the latest condition of the system by means of updating degradation data continuously, and obtain the explicit expression of RUL distribution. Finally, a numerical example and a practical case study are provided to show that the presented approach can effectively model degradation process for the individual system and obtain better results for RUL estimation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper scheduling communications in data gathering networks is analyzed. We study collecting information by a set of sensors, each of which stores the data in its memory buffer and then passes them to a base station. The network lifetime ends as soon as the first node is out of memory. We use a divisible load model to propose a communication scheduling algorithm that maximizes the system lifetime, and hence, the total amount of gathered information. The influence of the sensor communication rates on the obtained schedules is exposed in a series of computational experiments. [Copyright &y& Elsevier]

Abstract: In this paper, an approach to achieve fault diagnosis and Fault Tolerant Control in a typical bottle-filling plant using event based techniques is discussed. For this purpose, the plant is modeled using Hybrid Petri nets which enable study and analysis with regard to the working of the plant. Once effective modeling is done based on two different case studies considered, new algorithms are proposed to achieve fault diagnosis and Fault Tolerant Control on the models developed. Finally, performance measures with regard to the models proposed are evaluated to check the correctness of the models developed. Both analytical and numerical results are obtained which are highly useful to understand plant behavior. [Copyright &y& Elsevier]

Abstract: The tools of lattice theory are applied to readdress the static state feedback linearization problem for discrete-time nonlinear control systems. Unlike the earlier results that are based on differential geometry, the new tools are also applicable for nonsmooth systems. In case of analytic systems, close connections are established between the new results and those based on differential one-forms. The Mathematica functions have been developed that implement the algorithms/methods from this paper. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the minimum flow problem on network flows in which the lower arc capacities vary with time. We will show that this problem for set {0,1,…, T} of time points can be solved by at most n minimum flow computations, by combining of preflow-pull algorithm and reoptimization techniques (no matter how many values of T are given). Running time of the presented algorithm is O(n 2 m). [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: Any realistic model of learning from samples must address the issue of noisy data. AdaBoost is known as an effective method for improving the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost is prone to overfitting, especially in noisy domains. On the other hand, the kNN rule is one of the oldest and simplest methods for pattern classification. Nevertheless, it often yields competitive results, and in certain domains, when cleverly combined with prior knowledge, it has significantly advanced the state-of-the-art. In this paper, an edited AdaBoost by weighted kNN (EAdaBoost ) is designed where AdaBoost and kNN naturally complement each other. First, AdaBoost is run on the training data to capitalize on some statistical regularity in the data. Then, a weighted kNN algorithm is run on the feature space composed of classifiers produced by AdaBoost to achieve competitive results. AdaBoost is then used to enhance the classification accuracy and avoid overfitting by editing the data sets using the weighted kNN algorithm for improving the quality of training data. Experiments performed on ten different UCI data sets show that the new Boosting algorithm almost always achieves considerably better classification accuracy than AdaBoost. Furthermore, experiments on data with artificially controlled noise indicate that the new Boosting algorithm is robust to noise. [ABSTRACT FROM AUTHOR]

Abstract: The traditional trend of DNA computing aims at solving computationally intractable problems. The minimum bisection problem (MBP) is a well-known NP-hard problem, which is intended to partition the vertices of a given graph into two equal halves so as to minimize the number of those edges with exactly one end in each half. Based on a biologically inspired computational model, this paper describes a novel algorithm for the minimum bisection problem, which requires a time cost and a DNA strand length that are linearly proportional to the instance size. [Copyright &y& Elsevier]

Abstract: In this paper, an extension of the modified generalized predictive control (GPC) algorithm and a tuning strategy is presented. To take the plant dynamics such as under damped behavior and the effect of zeros into account, extension to the second order plus dead time (SOPDT) of the first order plus dead time (FOPDT) modified GPC method is proposed. It is shown that this method is computationally undemanding. Also, implementation is more straightforward than conventional GPC algorithms. Moreover, the proposed tuning strategy enables a fast implementation of the GPC with regard to nominal stability and desired performance. The simplicity of this strategy and its wide applicability makes it readily accessible to practitioners for utilization. Multiple simulation results are provided to show the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper studies online scheduling of unit length jobs on a parallel batching machine with the help of lookahead. The objective is to maximize the number of early jobs. Denote by the size of each batch with in the unbounded batching and in the bounded batching. In the lookahead model, at a time instant , an online algorithm can foresee all jobs that will arrive in the time segment . When , we show that a simple greedy online algorithm (independent of the value of ) has a best possible competitive ratio of , where is the number of jobs. This means that lookahead is useless when . When , we establish the upper bounds (for ) and (for ) of competitive ratios, and provide an online algorithm of competitive ratios (for ) and (for ). [Copyright &y& Elsevier]

Abstract: In this paper, we consider minimizing total weighted completion time criteria on a single machine. Jobs processing times are step function of its starting time and all jobs have a common due date. First, we present some new lemmas and dominance properties for this NP-hard problem, and then a memetic algorithm using these properties is developed. We compare the solutions of the memetic algorithm with optimal solutions obtained from complete enumeration. The results show that the average percentage error of the proposed algorithm from optimal solutions is about 2% and as the variance of processing time increase, the percentage errors decrease. [Copyright &y& Elsevier]

Abstract: This paper presents the market clearing of joint energy and reserves auctions and its mathematical formulation, focusing on a possible implementation of the Payment Cost Minimization (PCM). It also discusses another key point in debate: whether market clearing algorithm should minimize offer costs or payment costs? An aggregated simultaneous market clearing approach is proposed for provision of ancillary services as well as energy, which is in the form of Mixed Integer Nonlinear Programming (MINLP) formulation. In the MINLP formulation of the market clearing process, the objective function (Payment cost or offer cost) are optimized while meeting AC power flow constraints, system reserve requirements and lost opportunity cost (LOC) considerations. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS), and simulation studies are carried out to examine the effectiveness of each objective function. [Copyright &y& Elsevier]

Abstract: Discrete tomography deals with the reconstruction of images from their projections where the images are assumed to contain only a small number of grey values. In particular, there is a strong focus on the reconstruction of binary images (binary tomography). A variety of binary tomography problems have been considered in the literature, each using different projection models or additional constraints. In this paper, we propose a generic iterative reconstruction algorithm that can be used for many different binary reconstruction problems. In every iteration, a subproblem is solved based on at most two of the available projections. Each of the subproblems can be solved efficiently using network flow methods. We report experimental results for various reconstruction problems. Our results demonstrate that the algorithm is capable of reconstructing complex objects from a small number of projections. [Copyright &y& Elsevier]

Abstract: The distributed daemon model introduced by Burns in 1987 is a natural generalization of the central daemon model introduced by Dijkstra in 1974. In this paper, we show that a well-known shortest path algorithm is self-stabilizing under the distributed daemon model. Although this result has been proven only recently, the correctness proof provided here is from a different point of view and is much more concise. We also show that Bruell et al.’s center-finding algorithm is actually self-stabilizing under the distributed daemon model. Finally, we compute the worst-case stabilization times of the two algorithms under the distributed daemon model. [Copyright &y& Elsevier]

Abstract: The traffic assignment problem (TAP) is traditionally defined without side constraints. Although introducing side constraints into the TAP is used as a way of refining applied assignment models, it brings some computational difficulties that prevent the refined model from being applicable in real-case problems. In this paper an iterative solution algorithm is developed for TAP with linear side constraints. This method is based on implicitly considering the side constraints by means of adding a dynamic penalty function (DPF) to the link travel times. Each of the iterations of the algorithm is reduced to an unconstrained assignment followed by updating the penalty functions. The unconstrained assignment is carried out using the linearization method that has been derived from the complementarity formulation of TAP. The implementation of the algorithm and computational experiments on some well-known networks are also presented for two types of side constraint, i.e. link and node capacity constraints. For the link-type side constraint, compared to the reported results of the existing alternative approaches, a DPF algorithm finds feasible solutions with better Beckmann’s objective functions in less number of iterations. For the node-type constraints, the algorithm also shows good performance. [Copyright &y& Elsevier]

Abstract: The implicitly restarted harmonic Arnoldi algorithm by Morgan used those unwanted harmonic Ritz values as shifts—called Morgan’s harmonic shifts. In this paper, a new shift scheme is given for the harmonic Arnoldi algorithm. We first analyze the harmonic Ritz values of from the orthogonal complement of span of those wanted harmonic Ritz vectors with respect to , then present an implicitly restarted harmonic Arnoldi algorithm with as shifts. Finally, through the numerical experiments, we mainly draw comparisons on our algorithm and Morgan’s algorithm, and show our algorithm often performed better than Morgan’s one. [Copyright &y& Elsevier]

Abstract: Modeling and simulation is essential for understanding complex ecological systems. However, knowledge of the structure and behavior of these systems is limited, and models must be revised frequently as our understanding of a system improves. Moreover, the dynamic, spatial distribution of activity in very large systems necessitates mapping natural mechanisms as logically as possible onto computer structures. This paper describes theoretical and algorithmic tools for building component-based models and simulations of dynamic spatial phenomena. These methods focus attention on and exploit the irregular distribution of activity in ecological processes. We use the DEVS formalism as the basis for a component-based approach to modeling spatially distributed systems. DEVS is a mathematical theory of discrete-event systems that is well suited for describing large systems that are described by small parts with irregular, short-range interactions. This event-based approach to modeling leads naturally to efficient simulations algorithms which focus on the active parts of a large model. Ecological modeling benefits from these efficient the simulation algorithms and the reusability of the model’s basic components. Our event-based method is demonstrated with a physics-based model of fire spread. [Copyright &y& Elsevier]

Abstract: In this paper, we study the conditions in which the random hill-climbing algorithm (1 + 1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at a given iteration and with respect to the average optimization time. Our approach is applicable when the reproduction operator of an evolutionary algorithm is dominated by the mutation operator of the (1 + 1)-EA. In this case one can extend the lower bounds obtained for the expected optimization time of the (1 + 1)-EA to other EAs based on the dominated reproduction operator. This method is demonstrated on the sorting problem with HAM landscape and the exchange mutation operator. We consider several simple examples where the (1 + 1)-EA is the best possible search strategy in the class of the EAs. [Copyright &y& Elsevier]

Abstract: The iterative penalty method for Stokes equations is an effective algorithm. In this paper, we discuss the bilinear-constant velocity–pressure element for the Stokes equations and present the error estimates for the iterative penalty method using this element. The key technique we use is the local checkerboard pattern. Numerical experiments are given which are consistent with our analysis. [Copyright &y& Elsevier]

Abstract: In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. These changes and the numerous advantages of the distributed generation units (DGs) in term of their technology enhancement and economical considerations have created more incentives to use these kinds of generators than before. Therefore, it is necessary to study the impact of DGs on the power systems, especially on the distribution networks. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering DGs. The main objective of the DFR is to minimize the deviation of the bus voltage, the number of switching operations and the total cost of the active power generated by DGs and distribution companies. Since the DFR is a nonlinear optimization problem, we apply the particle swarm optimization (PSO) approach to solve it. The feasibility of the proposed approach is demonstrated and compared with other evolutionary methods such as genetic algorithm (GA), Tabu search (TS) and differential evolution (DE) over a realistic distribution test system. [Copyright &y& Elsevier]

Abstract: In this paper we construct an approximate solution to large Sylvester equations of the form . The construction uses a new variant of the block Arnoldi algorithm which exploits the near-breakdowns, that is, the near singularities in the generated basis. As a consequence, the algorithm eliminates the directions which do not contribute to the approximate solution by keeping in the generated basis only the “active” vectors detected by a criterion based on the residual associated with the approximate solution. The effectiveness of the proposed algorithm is demonstrated on several examples, including the case where the matrix B has a small or a large size. [Copyright &y& Elsevier]

Abstract: In stormwater quality modeling, estimating the confidence level in conceptual model parameters is necessary but difficult. The applicability and the effectiveness of a method for model calibration and model uncertainty analysis in the case of a four parameters lumped urban runoff quality model are illustrated in this paper. This method consists of a combination of the Metropolis algorithm for parameters’ uncertainties and correlation assessment and a variance-based method for global sensitivity analysis. The use of the Metropolis algorithm to estimate the posterior distribution of parameters through a likelihood measure allows the replicated Latin hypercube sampling method to compute the parameters’ importance measures. Calibration results illustrate the usefulness of the Metropolis algorithm in the assessment of parameters’ uncertainties and their interaction structure. The sensitivity analysis demonstrates the insignificance of some parameters in terms of driving the model to have a good conformity with the data. This method provides a realistic evaluation of the conceptual description of the processes used in models and a progress in our capability to assess parameters’ uncertainties. [Copyright &y& Elsevier]

Abstract: In this paper we present a tool to carry out the multifractal analysis of binary, two-dimensional images through the calculation of the Rényi D(q) dimensions and associated statistical regressions. The estimation of a (mono)fractal dimension corresponds to the special case where the moment order is q =0. [Copyright &y& Elsevier]

Abstract: This paper examines numerically and theoretically the application of truncated Singular Value Decomposition (SVD) in a sequential form. The Sequential SVD algorithm presents two tunable hyperparameters: the number of future temperature and the rank of the truncated sensitivity matrix . The regularization effect of both hyperparameters is consistent with the data filtering interpretation by truncated SVD (reported by Shenefelt [Internat. J. Heat Mass Transfer 45 (2002) 67]). This study reveals that the most suitable reduced rank is “one”. Under this assumption , the sequential procedure proposed, presents several advantages with respect to the standard whole-domain procedure: The search of the optimum rank value is not required. The simplification of the model is the maximum that can be achieved. The unique tunable hyperparameter is the number of future temperatures, and a very simple algorithm is obtained. This algorithm has been compared to: Function Specification Method (FSM) proposed by Beck and the standard whole-domain SVD. In this comparative study, the parameters considered have been: the shape of the input, the noise level of measurement and the size of time step. In all cases, the FSM and sequential SVD algorithm give very similar results. In one case, the results obtained by the sequential SVD algorithm are clearly superior to the ones obtained by the whole-domain algorithm. [Copyright &y& Elsevier]