Search

Distributed nature of wireless sensor network raises a number of design challenges, especially when energy-efficiency and Quality of Service requirements are to be taken into consideration. These challenges can only be met by allowing closer cooperation and mutual adaptation between the protocol layers, referred to as a cross-layer design paradigm. In this paper, we explain the operating stages for adaptive sleep with adaptive modulation based on the MAC layer protocol. By using adaptive sleep with adaptive modulation the total time for completing one packet is adaptively reduced. Therefore, not only the transmission time is adapted by adaptive modulation, but also the sleep time is varied by adaptive sleep. A cross-layer, optimization scheme, based on adaptive sleep with adaptive modulation along with constellation rearrangement and power control, is proposed in this paper for minimizing energy cost and enhancing the network longevity. The adaptive sleep with adaptive modulation along with constellation rearrangement algorithm changes the modulation scheme dynamically by using constellation rearrangement while adjusting the node sleep periods and power levels. The paper considers several variations of these schemes and analyzes and compares their performance under various traffic intensity based on extensive computer simulations. Finally the proposed scheme is evaluated through NS2 simulations in terms of throughput. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Hierarchical Particle Swarm Optimization (HPSO) algorithm model for multi- modal function optimization. All particles will be classified into several groups, these groups operated at two levels: one level is to find location of global optima clusters with its particles; the other is to exactly distinguish multiple global optima in clusters. By this mechanism, algorithm can explore the search space at different level of scales simultaneously; all the global optima can be detected effectively. In addition, the algorithm is easy to be controlled; only two parameters are needed to be predefined before the running of algorithm and algorithm's performance is not sensitive to the influence caused by these two parameters. We tested our algorithm on several benchmark functions and compared it with some well-known methods for these functions. Experimental results showed that our algorithm could achieve an overall good performance. We also analyzed the effect of the parameters for our algorithm performance at the end of our paper. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problem is a kind of common optimization problem in science and engineering. This paper explores the improvement strategy of multi-objective ant colony algorithm and proposes an Elitist Multi-objective Ant Colony Optimization (EMOACO). This method proposes to improve ant colony fitness based on Pareto non-dominated set, performs local search on every individual generated in the ant colony algorithm and accelerates the parallel search of multiple objectives by adopting elite selection strategy in order to increase its search rate. The experimental result shows that the algorithm of this paper is effective and that it makes some improvements in global optimization capacity and population diversity compared with the basic multi-objective ant colony algorithm, it can quickly converge to Pareto optimal solution and provide a reliable basis for the decision making. [ABSTRACT FROM AUTHOR]

The increasing complexities of wellbore geometry imply an increasing well cost. It has become more important than ever to achieve an increased rate of penetration (ROP) and, thus, reduced cost per foot. To achieve maximum ROP, an optimization of drilling parameters is required as the well is drilled. While there are different optimization techniques, there is no acceptable universal mathematical model that achieves maximum ROP accurately. Usually, conventional mathematical optimization techniques fail to accurately predict optimal parameters owing to the complex nature of downhole conditions. To account for these uncertainties, evolutionary-based algorithms can be used instead of mathematical optimizations. To arrive at the optimum drilling parameters efficiently and quickly, the metaheuristic evolutionary algorithm, called the "shuffled frog leaping algorithm, (SFLA) is used in this paper. It is a type of rising swarm-intelligence optimizer that can optimize additional objectives, such as minimizing hydromechanical specific energy. In this paper, realtime gamma ray data are used to compute values of rock strength and bit-tooth wear. Variables used are weight on bit (WOB), bit rotation (N), and flow rate (Q). Each variable represents a frog. The value of each frog is derived based on the ROP models used individually or simultaneously through iteration. This optimizer lets each frog (WOB, N, and Q) jump to the best value (ROP) automatically, thus arriving at the near optimal solution. The method is also efficient in computing optimum drilling parameters for different formations in real time. The paper presents field examples to predict and estimate the parameters and compares them to the actual real-time data. [ABSTRACT FROM AUTHOR]

The connection between the sparsest solution to an underdetermined system of linear equations and the weighted ℓ1-minimization problem is established in this paper. We show that seeking the sparsest solution to a linear system can be transformed to searching for the densest slack variable of the dual problem of weighted ℓ1-minimization with all possible choices of nonnegative weights. Motivated by this fact, a new reweighted ℓ1-algorithm for the sparsest solutions of linear systems, going beyond the framework of existing sparsity-seeking methods, is proposed in this paper. Unlike existing reweighted ℓ 1-methods that are based on the weights defined directly in terms of iterates, the new algorithm computes a weight in dual space via certain convex optimization and uses such a weight to locate the sparsest solutions. It turns out that the new algorithm converges to the sparsest solutions of linear systems under some mild conditions that do not require the uniqueness of the sparsest solutions. Empirical results demonstrate that this new computational method remarkably outperforms ℓ 1-minimization and stands as one of the very efficient sparsity-seeking algorithms for the sparsest solutions of systems of linear equations. [ABSTRACT FROM AUTHOR]

The present study is focused on mining planning with an emphasis on the graph theory model proposed by Lerchs-Grossmann. The original paper published by Lerchs-Grossmann about determination of optimum final pit does not report the computational algorithm to solve the problem. This paper discusses and presents an algorithm based on the maximum flow graph computational work from Ford Fulkerson. The main steps for solving the problem and the results of the two-dimensional models are discussed. [ABSTRACT FROM AUTHOR]

With various emerging mobile devices, the visual content have be to resized into different sizes or aspect ratios for good viewing experiences. In this paper, we propose a new multioperator retargeting algorithm by using four retargeting operators of seam carving, cropping, warping, and scaling iteratively. To determine which retargeting operator should be used at each iteration, we adopt structural similarity (SSIM) to evaluate the similarity between the original and retargeted images. The retargeting operator sequence is constructed based on the four types of retargeting operators by an optimization process. Since the sizes of original and retargeted images are different, scale-invariant feature transform flow is used for dense correspondence between the original and retargeted images for similarity evaluation. Additionally, visual saliency is used to weight SSIM results based on the characteristics of the human visual system. Experimental results on a public image retargeting database have shown the promising performance of the proposed multioperator retargeting algorithm. [ABSTRACT FROM AUTHOR]

In this paper we describe an algorithm visiting all numerical semigroups up to a given genus using a well-suited representation. The interest of this algorithm is that it fits particularly well the architecture of modern computers allowing very large optimizations: we obtain the number of numerical semigroups of genus g ≤ 67 and we confirm the Wilf conjecture for g ≤ 60. [ABSTRACT FROM AUTHOR]

Cloud computing provides a new opportunity for Video Service Providers (VSP) to running compute-intensive video applications in a cost effective manner. Under this paradigm, a VSP may rent virtual machines (VMs) from multiple geo-distributed datacenters that are close to video requestors to run their services. As user demands are difficult to predict and the prices of the VMs vary in different time and region, optimizing the number of VMs of each type rented from datacenters located in different regions in a given time frame becomes essential to achieve cost effectiveness for VSPs. Meanwhile, it is equally important to guarantee users' Quality of Experience (QoE) with rented VMs. In this paper, we give a systematic method called Dynamical Request Redirection and Resource Provisioning (DYRECEIVE) to address this problem. We formulate the problem as a stochastic optimization problem and design a Lyapunov optimization framework based online algorithm to solve it. Our method is able to minimize the long-term time average cost of renting cloud resources while maintaining the user QoE. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing. Extensive experiments shows that our method is adaptive to request pattern changes along time and outperforms existing algorithms. [ABSTRACT FROM AUTHOR]

Concurrent designing of tolerance has become a vital concern in product and process development due to the relationship between quality, functionality and product cost. It is one of the well explored areas in combinatorial optimization. In this paper, a recently developed optimization algorithm, called Bat algorithm (BA), is used for optimizing the tolerance based on concurrent objectives to minimize the manufacturing cost, present worth of expected quality loss and quality loss. The mechanical assemblies such as Bevel gear assembly (A), Gear box assembly (B) and Suction union assembly (C) are considered to demonstrate the proposed algorithm. It is found that the BA has produced better results than other methods in initial generations for concurrent tolerance problems. [ABSTRACT FROM AUTHOR]

In general, a multi-object tracking problem in a network of cameras consists of two steps. First, objects are tracked in each camera and tracklets, which are the traces of each object in each camera, are extracted by any common tracking algorithm. In the second step, the extracted tracklets are associated and persistent trace of the objects are obtained. In this paper, our focus is concentrated on the second step where we propose a multiphase variational method and compare it against single-phase variational model applied for the association. Both methods use the extracted tracklets as the input and the persistent trace of objects are evaluated by associating the corresponding tracklets of objects. The association is formulated by optimizing a variational energy function, which is constructed based on appearance and motion model of the objects. The optimization problem is solved by, first converting the variational energy function into an Ordinary Differential Equation (ODE) employing the Euler-Lagrange equation; then solving the obtained ODE through numerical methods. Experimental results on the real and synthetic datasets demonstrate that the multiphase model yields more complete tracking results, while the single-phase model yields more accurate tracking results. [ABSTRACT FROM AUTHOR]

The design of water distribution networks (WDNs) is a complex nonlinear problem that has been solved using optimization techniques attempting to find near-globally optimal solutions. This paper demonstrates that relying on engineering judgment, with very limited use of optimization, can generate reasonable quality approximations of the Pareto front of multiobjective WDN design problems without intensive computational requirements. First the results of the implementation of a previous implementation of the engineering judgment approach to the Battle of Background Leakage Assessment for Water Networks (BBLAWN) design competition is summarized. Additionally, a simplified version of the BBLAWN problem is solved using both the engineering judgment approach and a multiobjective optimization algorithm and the corresponding Pareto fronts are compared. Relative to other BBLAWN solutions, the engineering judgment solution produced a high-quality result with a very small computational budget and was able to identify a more realistic number of changes in the WDN (~10% of the network) compared with BBLAWN teams relying on global optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper outlines a new computational aerodynamic design optimisation algorithm using a novel method of parameterising a computational mesh using ‘control nodes’. The shape boundary movement as well as the mesh movement is coupled to the movement of user–defined control nodes via a Delaunay Graph Mapping technique. A Modified Cuckoo Search algorithm is employed for optimisation within the prescribed design space defined by the allowed range of control node displacement. A finite volume compressible Navier–Stokes solver is used for aerodynamic modelling to predict aerodynamic design ‘fitness’. The resulting coupled algorithm is applied to a range of test cases in two dimensions including aerofoil lift–drag ratio optimisation intake duct optimisation under subsonic, transonic and supersonic flow conditions. The discrete (mesh–based) optimisation approach presented is demonstrated to be effective in terms of its generalised applicability and intuitiveness. [ABSTRACT FROM AUTHOR]

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. In this paper, inspired by the particle swarm optimization (PSO), the proposed algorithm uses the best individuals among the entire population to enhance the convergence rate of the standard cuckoo search algorithm. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents of the CS do not directly use this information but the global best solution in the CS is stored at the each iteration. The global best solutions are used to add into the Information flow between the nest helps increase global and local search abilities of the new approach. Therefore, in the first component, the neighborhood information is added into the new population to enhance the diversity of the algorithm. In the second component, two new search strategies are used to balance the exploitation and exploration of the algorithm through a random probability rule. In other aspect, our algorithm has a very simple structure and thus is easy to implement. To verify the performance of PSCS, 30 benchmark functions chosen from literature are employed. The results show that the proposed PSCS algorithm clearly outperforms the basic CS and PSO algorithm. Compared with some evolution algorithms (CLPSO, CMA-ES, GL-25, DE, OXDE, ABC, GOABC, FA, FPA, CoDE, BA, BSA, BDS and SDS) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. In the last part, experiments have been conducted on two real-world optimization problems including the spread spectrum radar poly-phase code design problem and the chaotic system. Simulation results demonstrate that the proposed algorithm is very effective. [ABSTRACT FROM AUTHOR]

Distributed optimization in multi-agent systems under sparsity constraints has recently received a lot of attention. In this paper, we consider the in-network minimization of a continuously differentiable nonlinear function which is a combination of local agent objective functions subject to sparsity constraints on the variables. A crucial issue of in-network optimization is the handling of the communications, which may be expensive. This calls for efficient algorithms, that are able to reduce the number of required communication links and transmitted messages. To this end, we focus on asynchronous and randomized distributed techniques. Based on consensus techniques and iterative hard thresholding methods, we propose three methods that attempt to minimize the given function, promoting sparsity of the solution: asynchronous hard thresholding (AHT), broadcast hard thresholding (BHT), and gossip hard thresholding (GHT). Although similar in many aspects, it is difficult to obtain a unified analysis for the proposed algorithms. Specifically, we theoretically prove the convergence and characterize the limit points of AHT in regular networks under some proper assumptions on the functions to be minimized. For BHT and GHT, instead, we characterize the fixed points of the maps that rule their dynamics in terms of stationary points of original problem. Finally, we illustrate the implementation of our techniques in compressed sensing and present several numerical results on performance and number of transmissions required for convergence. [ABSTRACT FROM PUBLISHER]

This paper presents a novel optimization framework for classic windows using a bio-inspired methodology, the firefly algorithm. Classic windows are widely used as sidelobe control for signals undergoing discrete Fourier transform processing. In general, the lower sidelobes of nonrectangular windows have been achieved at the cost of broadening of the mainlobe width (MW). Here, the novel optimized windows allow a better peak sidelobe ratio without MW broadening. The simulation results show the effectiveness of the proposal for sidelobe reduction for the purpose of radar range imaging while preserving resolution. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms. [ABSTRACT FROM AUTHOR]

This paper develops the Decentralized Linearized Alternating Direction Method of Multipliers (DLM) that minimizes a sum of local cost functions in a multiagent network. The algorithm mimics operation of the decentralized alternating direction method of multipliers (DADMM) except that it linearizes the optimization objective at each iteration. This results in iterations that, instead of successive minimizations, implement steps whose cost is akin to the much lower cost of the gradient descent step used in the distributed gradient method (DGM). The algorithm is proven to converge to the optimal solution when the local cost functions have Lipschitz continuous gradients. Its rate of convergence is shown to be linear if the local cost functions are further assumed to be strongly convex. Numerical experiments in least squares and logistic regression problems show that the number of iterations to achieve equivalent optimality gaps are similar for DLM and ADMM and both much smaller than those of DGM. In that sense, DLM combines the rapid convergence of ADMM with the low computational burden of DGM. [ABSTRACT FROM PUBLISHER]

Recently, the hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) has been proposed for discovery of classification rules. An improved version of this hybrid scheme, PSO/ACO2 algorithm, can directly cope with nominal attributes without converting them into numerical ones. Although PSO/ACO2 can handle nominal values, it suffers from high computational complexity for large datasets. Beside variety of classification methods which exist to provide more compact set of rules, this study propose an approach which reduces the computational complexity of PSO/ACO2 in order to make it suitable for classification of large datasets. This work is developed the K-mode as a method of sampling from the datasets. In this regard a modification is employed to this algorithm in order to decrease the computational time as well as maintaining the accuracy of the algorithm. Further contribution of this paper is utilizing a new fitness measure for the algorithm. This measure has a robust theoretical background. The combination of the proposed modified K-mode method and the introduced fitness measure led to speed the obtained results up. The experimental result shows the efficiency of the proposed algorithm in comparison with its competitors. [ABSTRACT FROM AUTHOR]

This paper describes a modified version of an algorithm suggested earlier by the authors for optimizing of ads allocation in sponsored search on the main page of search results in response to user search queries to a web search engine. It is demonstrated that the modification of the algorithm reduces appreciably the searching procedure and the algorithm complexity. And finally, the new algorithm undergoes experimental testing on real data sets provided by Yandex. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization algorithm based on linear matrix inequalities (LMIs) to be implemented in a shunt active power filter (SAPF) enabling it to perform a selective compensation of nonefficient powers when the rated power of the SAPF is limited. The system uses the IEEE Standard 1459 to identify the different power terms (active, reactive, unbalance, and harmonics) demanded by local loads in a three-phase four-wire system. Then, the algorithm solves a quadratically constrained quadratic program by means of an LMI formulation and calculates the optimal reference currents that allow the SAPF to cancel a selection of the previously identified nonefficient power terms. For this selection, the algorithm presents different weighting coefficients in the cost index to be optimized. These coefficients assign a relative importance to each different nonefficient power term, giving priority to some over the others. Moreover, the optimization performed in this work to calculate the reference currents considers that the SAPF presents a power limitation. Finally, the reference currents' tracking has been implemented using a proportional feedforward controller which assures a constant commutation frequency of the SAPF. Experimental results demonstrate the validity of the proposal. [ABSTRACT FROM PUBLISHER]

This paper considers the vehicle routing problem with soft time windows, a challenging routing problem where customers' time windows may be violated at a certain cost. The vehicle routing problem with soft time windows has a lexicographic objective function, aimed at minimizing first the number of routes, then the number of violated time windows, and finally the total routing distance. We present a multistage very large-scale neighborhood search for this problem. Each stage corresponds to a variable neighborhood descent over a parameterizable very large-scale neighborhood. These neighborhoods contain an exponential number of neighbors, as opposed to classical local search neighborhoods. Often, searching very large-scale neighborhoods can produce local optima of a higher quality than polynomial-sized neighborhoods can. Furthermore, we use a sophisticated heuristic to determine service start times allowing us to minimize the number of violated time windows. We test our approach on a number of different problem types, and compare the results to the relevant state of the art. The experimental results show that our algorithm improves best known solutions on 53% of the most studied instances. Many of these improvements stem from a reduction of the number of vehicles, a critical objective in vehicle routing problems. [ABSTRACT FROM AUTHOR]

Research on complex system optimization has focused on areas including algorithms, coordination strategies, and communication tools. This paper considers optimization from the perspective of information coordination during the solution process. This work aims to determine the potential impact of factors including the role of a system facilitator in managing system-level tradeoffs, the influence of an ideal information scenario, the choice of system variables, and the amount and form of passed information. The goal is not to develop the "best" communication structure or optimization algorithm, but to understand the significance of these factors. A traditional multidisciplinary design optimization coordination framework, a game theoretic approach, and a modified game theory approach are used to mimic design team behaviors. Results suggest these factors can influence the coordination process outcomes: The choice of system variable plays a significant role in the optimality of the final design; perfect information does not necessarily reduce the number of iterations or improve optimality; a system facilitator aids in making better design tradeoffs, though it is possible to achieve optimality without one; and the stability of the design cycle depends on the amount of passed information. This suggests a design team should carefully analyze the initial design space to determine an appropriate design formulation before applying optimization. [ABSTRACT FROM AUTHOR]

This paper proposes a convex optimization algorithm to minimize the v-gap metric for parameter estimation of a SISO system using frequency-domain measurements. According to a geometrical interpretation for this metric, its evaluation for two SISO linear systems is the supreme of sine values of the apertures between their normalized right graph symbols. As a result, the minimization of the v-gap metric becomes that of the maximum of sine values of those apertures at sampling frequencies and can be efficiently solved by a sequence of feasibility problems with a decreasing line search. Compared with the related algorithms, the proposed one has global convergence and much lower computational loads. Finally, a numerical simulation shows the effectiveness of the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new three-user network information flow model, referred to as the triangular multiple-input–multiple-output (MIMO) relay (TMR) channel, which consists of three users and three relays equipped with nU and nR antennas, respectively. Each user sends two independent messages to the other two users via the adjacent relays in two time slots, which are referred to as the multiple-access and broadcast stages. We derive a novel simultaneous signal and interference alignment for the proposed TMR channel in a scenario where there are fewer antennas at each relay than at each user (nR < nU ) . An optimized pseudo-inverse scheme based on an efficient gradient projection algorithm is proposed to solve the simultaneous alignment problem. By deriving a gradient over weighted sum-rate maximization and applying a gradient descent method, the optimal beamforming vectors are obtained to maximize the effective signal-to-noise ratios. Furthermore, to obtain rapid convergence speed and reduce computational complexity, we introduce a quasi-Newton method, which is referred to as the Broyden–Fletcher–Goldfarb–Shanno algorithm, by approximating the Hessian matrix of a pure Newton method. The convergence of the proposed gradient algorithm is guaranteed by proposing a line search algorithm. Finally, a performance evaluation shows that the proposed scheme offers a higher sum rate, produces a better outage probability, and achieves a higher multiplexing gain than the existing schemes. [ABSTRACT FROM PUBLISHER]

In this paper, a practical modeling and robust controller optimization strategy is presented for an ultraprecision positioning stage with position-dependent dynamics to achieve ultraprecision positioning accuracy. A linear-fractional-transformation structured uncertainty modeling procedure is proposed to describe the varying dynamics of the stage. The modeling process involves the global curve fitting of frequency response functions and dimensionality reduction for the uncertainty structure so that the uncertainty set could be minimized. Then, a robust loop-shaping controller optimization method is presented to improve the control performances. The optimization objective includes the control bandwidth and the disturbance rejection ability, and $\mu$ analysis is employed as a nonconservative robust condition with respect to the structured uncertainty. A genetic algorithm is then utilized to determine the optimal parameters of the controller. Comparative experiments on a developed ultraprecision positioning stage are finally conducted, and the results validate that significant improvements on rising time, settling time, and positioning accuracy have been achieved. [ABSTRACT FROM PUBLISHER]

The maximum power point (MPP) tracking function allows one to extract the maximum power from the photovoltaic (PV) system in any operating condition. The perturb and observe technique is one of the mainly used algorithms aimed at this function. Its performances depend on the values of the two design parameters, which are the amplitude and the frequency of the perturbations that it applies in identifying the position of the MPP. In this paper, a method for the online optimization of the perturbation period is presented. It is based on the identification of the whole system, including the PV source and the dc/dc converter controlling it. The system impulse and frequency responses are evaluated by using the cross-correlation method. Such a technique, as well as the tracking algorithm, is effectively implemented by using a field-programmable gate array, and it is validated by means of simulation and experimental results. [ABSTRACT FROM PUBLISHER]

The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation. [ABSTRACT FROM AUTHOR]

Exiting parallel DEM preprocessing algorithms that do not consider parallel granularity. This paper presents a parallel DEM preprocessing algorithm with granularity control based on the a-nalysis of the sequential algorithm proposed by Moran and Vezina (M&-V algorithm). A Message Passing Interface (MPD library is applied to implement the parallel algorithm. The parallel performance of the proposed algorithm is assessed by two gridded DEMs with different sizes on a multi-node Linux cluster. The application results show that the parallel M&-V algorithm can complete the computing tasks when filling sinks and removing flat areas at any granuality, and it outputs an optimal granularity to achieve the best parallel performance for a given DEM dataset. [ABSTRACT FROM AUTHOR]

Given a symmetric and positive (semi)definite n-by-n matrix M and a vector, in this paper, we consider the matrix splitting method for solving the second-order cone linear complementarity problem (SOCLCP). The matrix splitting method is among the most widely used approaches for large scale and sparse classical linear complementarity problems, and its linear convergence is proved by [Luo and Tseng, SIAM J. Control Optim., 30 (1992), pp. 408-425]. Our first contribution is to prove that, when the general matrix splitting algorithm is applied to SOCLCP with M symmetric and positive definite, it also converges at least linearly. Numerically, our second contribution is to propose a special and efficient matrix splitting algorithm, the block successive overrelaxation method, for solving the SOCLCP. The algorithm makes good use of the underlying geometry of the SOCLCP and each iteration only involves solving triangular linear systems and requires 0 (n²) flops; moreover, the algorithm does not destroy the sparse structure of M and is able to exploit the sparsity effectively. Our code BSOR_BN_L based on this method is tested against four other state-of-the-art methods on problems with dense, ill-conditioned symmetric and positive definite matrices, as well as on problems with large scale sparse, symmetric and positive (semi)definite matrices. The numerical results are quite encouraging and BSOR_BN_L exhibits a very good performance in terms of its speed, accuracy, and efficiency in exploiting the sparsity of M . [ABSTRACT FROM AUTHOR]

Polar codes are the first error-correcting codes to provably achieve channel capacity, asymptotically in code length, with an explicit construction. However, under successive-cancellation decoding, polar codes require very long code lengths to compete with existing modern codes. Nonetheless, the successive cancellation algorithm enables very-low-complexity implementations in hardware, due to the regular structure exhibited by polar codes. In this paper, we present an improved architecture for successive-cancellation decoding of polar codes, making use of a novel semi-parallel, encoder-based partial-sum computation module. We also provide quantization results for realistic code length N=2^15, and explore various optimization techniques such as a chained processing element and a variable quantization scheme. This design is shown to scale to code lengths of up to N=2^21, enabled by its low logic use, low register use and simple datapaths, limited almost exclusively by the amount of available SRAM. It also supports an overlapped loading of frames, allowing full-throughput decoding with a single set of input buffers. [ABSTRACT FROM PUBLISHER]

Source mask optimization (SMO) is a leading resolution enhancement technique in immersion lithography at the 45-nm node and beyond. Current SMO approaches, however, fix the numerical aperture (NA), which has a strong impact on the depth of focus (DOF). A higher NA could realize a higher resolution but reduce the DOF; it is very important to balance the requirements of NA between resolution and the DOF. In addition, current SMO methods usually result in complicated source and mask patterns that are expensive or difficult to fabricate. This paper proposes a parametric source-mask-NA co-optimization (SMNO) method to improve the pattern fidelity, extend the DOF, and reduce the complexity of the source and mask. An analytic cost function is first composed based on an integrative vector imaging model, in which a differentiable function is applied to formulate the source and mask patterns. Then, the derivative of the cost function is deduced and a gradient-based algorithm is used to solve the SMNO problem. Simulation results show that the proposed SMNO can achieve the optimum combination of parametric source, mask, and NA to maintain high pattern fidelity within a large DOF. In addition, the complexities of the source and mask are effectively reduced after optimization. [ABSTRACT FROM AUTHOR]

The smoothing-type algorithm has been successfully applied to solve various optimization problems. Based on a new class of smoothing functions, in this paper, we propose a smoothing-type algorithm with a mixed line search for solving the nonlinear complementarity problem. Under suitable conditions, the proposed algorithm is well-defined and maintains global convergence and local superlinear convergence. Preliminary numerical results are also reported and demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model-hybrid approach for nonlinear optimization that employs both trust region method and quasi-Newton method, which can avoid possibly resolve the trust region subproblem if the trial step is not acceptable. In particular, unlike the traditional trust region methods, the new approach does not use a single approximate model from beginning to the end, but instead employs quadratic model or conic model at every iteration adaptively. We show that the new algorithm preserves the strong convergence properties of trust region methods. Numerical results are also presented. [ABSTRACT FROM AUTHOR]

Cultural Algorithms and Tabu search algorithms are both powerful tools to solve intricate constrained engineering and large-scale multi-modal optimization problems. In this paper, we introduce a hybrid approach that combines Cultural Algorithms and Tabu search (CA-TS). Here, Tabu Search is used to transform History Knowledge in the Belief Space from a passive knowledge source to an active one. In each generation of the Cultural Algorithm, we calculate the best individual solution and then seek the best new neighbor of that solution in the social network for that population using Tabu search. In order to speed up the convergence process through knowledge dissemination, simple forms of social network topologies were used to describe the connectivity of individual solutions. This can reduce the number of needed generations while maintaining accuracy and increasing the search radius when needed. The integration of the Tabu search algorithm as a local enhancement process enables CA-TS to leap over false peaks and local optima. The proposed hybrid algorithm is applied to a set of complex non-linear constrained engineering optimization design problems. Furthermore, computational results are discussed to show that the algorithm can produce results that are comparable or superior to those of other well-known optimization algorithms from the literature, and can improve the performance and the speed of convergence with a reduced communication cost. [ABSTRACT FROM AUTHOR]

Reducing the carbon footprint of data centers is becoming a primary goal of large IT companies. Unlike traditional energy sources, renewable energy sources are usually intermittent and unpredictable. How to better utilize the green energy from these renewable sources in data centers is a challenging problem. In this paper, we exploit the opportunities offered by geographical load balancing, opportunistic scheduling of delay-tolerant workloads, and thermal storage management in data centers to facilitate green energy integration and reduce the cost of brown energy usage. Moreover, bandwidth cost variations between users and data centers are considered. Specifically, this problem is first formulated as a stochastic program, and then, an online control algorithm based on the Lyapunov optimization technique, called Stochastic Cost Minimization Algorithm (SCMA), is proposed to solve it. The algorithm can enable an explicit trade-off between cost saving and workload delay. Numerical results based on real-world traces illustrate the effectiveness of SCMA in practice. [ABSTRACT FROM PUBLISHER]

The design of a water distribution network (WDN) is conceptually a multiobjective problem characterized by stringent constraints. Even in its most simple formulation, which requires only the maintenance of minimum pressures, it is difficult to solve, and the simplifications introduced often impair the practicality of the solutions. The paper aims at integrating an engineering approach to the optimization problem by generating pseudofronts in the hydraulically feasible region of the search space. The optimization algorithms provide an entirely feasible trade-off between cost and considered constraints, which may be directly used by a hypothetical decision maker. This engineering approach requires a fast tool to search for feasible solutions: the recently developed genetic heritage by stochastic evolution transmission (GHEST) algorithm, used herein, has proved to be effective and efficient in solving the optimal design problem of WDNs. Tests are carried out on two large-size water distribution networks (Balerma and Modena), using the GHEST algorithm, modified to account for several objectives. [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of different configurations of initial conditions for the optimization algorithm based on particle swarms with vorticity behavior. The proposed algorithm combines the gradient-based search and particle swarm behavior, thus, this algorithm can be affected by the initial conditions for the particles. Finally, a 2D test function is used to observe the characteristics of the algorithm. [ABSTRACT FROM AUTHOR]