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This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush–Kuhn–Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator. [ABSTRACT FROM AUTHOR]

Many statistical estimation procedures lead to nonconvex optimization problems. Algorithms to solve these problems are often guaranteed to output a stationary point of the optimization problem. Oracle inequalities are an important theoretical instrument to assess the statistical performance of an estimator. Oracle results have focused on the theoretical properties of the uncomputable (global) minimum or maximum. In this paper, a general framework used for convex optimization problems to derive oracle inequalities for stationary points is extended. A main new ingredient of these oracle inequalities is that they are sharp: they show closeness to the best approximation within the model plus a remainder term. We apply this framework to different estimation problems. [ABSTRACT FROM AUTHOR]

This paper presents an efficient algorithm for solving a balanced regularization problem in the frame-based image restoration. The balanced regularization is usually formulated as a minimization problem, involving an \ell2 data-fidelity term, an \ell1 regularizer on sparsity of frame coefficients, and a penalty on distance of sparse frame coefficients to the range of the frame operator. In image restoration, the balanced regularization approach bridges the synthesis-based and analysis-based approaches, and balances the fidelity, sparsity, and smoothness of the solution. Our proposed algorithm for solving the balanced optimal problem is based on a variable splitting strategy and the classical alternating direction method. This paper shows that the proposed algorithm is fast and efficient in solving the standard image restoration with balanced regularization. More precisely, a regularized version of the Hessian matrix of the \ell2 data-fidelity term is involved, and by exploiting the related fast tight Parseval frame and the special structures of the observation matrices, the regularized Hessian matrix can perform quite efficiently for the frame-based standard image restoration applications, such as circular deconvolution in image deblurring and missing samples in image inpainting. Numerical simulations illustrate the efficiency of our proposed algorithm in the frame-based image restoration with balanced regularization. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a theoretic and numerical study of utilizing squeezing and phase shift in coherent feedback control of linear quantum optical systems. A quadrature representation with built-in phase shifters is proposed for such systems. Fundamental structural characterizations of linear quantum optical systems are derived in terms of the new quadrature representation. These results reveal considerable insights into the issue of the physical realizability of such quantum systems. The problem of coherent quantum linear quadratic Gaussian (LQG) feedback control studied in H. I. Nurdin, M. R. James, and I. R. Petersen, Automatica, IFAC, 45 (2009), pp. 1837-1846; G. Zhang and M. R. James, IEEE Trans. Automat. Control, 56 (2011), pp. 1535-1550 is reinvestigated in depth. First, the optimization methods in these papers are extended to a multistep optimization algorithm which utilizes ideal squeezers. Second, a two-stage optimization approach is proposed on the basis of controller parametrization. Numerical studies show that closed-loop systems designed via the second approach may offer LQG control performance even better than that when the closed-loop systems are in the vacuum state. When ideal squeezers in a closed-loop system are replaced by (more realistic) degenerate parametric amplifiers, a sufficient condition is derived for the asymptotic stability of the resultant new closed-loop system; the issue of performance convergence is also discussed in the LQG control setting. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we try to solve the semidefinite program with box constraints. Since the traditional projection method for constrained optimization with box constraints is not suitable to the semidefinite constraints, we present a new algorithm based on the feasible direction method. In the paper, we discuss two cases: the objective function in semidefinite programming is linear and nonlinear, respectively. We establish the convergence of our algorithm, and report the numerical experiments which show the effectiveness of the algorithm. [Copyright &y& Elsevier]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

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: This paper deals with the use of reduced models for solving some optimal control problems. More precisely, the reduced model is obtained through the modal identification method. The test case which the algorithms is tested on is based on the flow over a backward-facing step. Though the reduction for the velocity fields for different Reynolds numbers is treated elsewhere , only the convection–diffusion equation for the energy problem is treated here. The model reduction is obtained through the solution of a gradient-type optimization problem where the objective function gradient is computed through the adjoint-state method. The obtained reduced models are validated before being coupled to optimal control algorithms. In this paper the feedback optimal control problem is considered. A Riccati equation is solved along with the Kalman gain equation. Additionally, a Kalman filter is performed to reconstruct the reduced state through previous and actual measurements. The numerical test case shows the ability of the proposed approach to control systems through the use of reduced models obtained by the modal identification method. [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 propeller design is an activity which nowadays presents ever increasing challenges to the designer, involving not only the usual mechanical characteristics fulfillment and cavitation erosion avoidance, but also other cavitation side effects, such as radiated noise and/or pressure pulses. Moreover, in some cases propeller characteristics have to be optimized in correspondence to very different functioning points, including considerably off-design conditions, hardly captured by conventional design methods. In the present paper, a recently presented method, based on the coupling between a multiobjective optimization algorithm and a panel code, is applied to the design of a CPP propeller at different pitch settings, with the aim of reducing the cavitating phenomena and, consequently, the resultant radiated noise. Particular attention has been devoted to the slow speed (low pitch) condition, obtained at constant RPM, and characterized by considerable radiated noise and vibrations related to face cavitation. Numerical results are validated by means of an experimental campaign, testing both the original and the optimized geometry in terms of cavitation extent and radiated noise. Experimental results confirm the numerical predictions, attesting the capability of the method to assess propeller functioning characteristics, thus representing a very useful tool for the designer in correspondence of challenging problems. [Copyright &y& Elsevier]

Abstract: This paper describes a new numerical method for the design of multi-step stamping tools, in which the optimization approach is based on the Response Surface Method (RSM) with Kriging interpolation as well as the Sequential Quadratic Programming (SQP) algorithm. The present work attempts to provide a reliable methodology for the optimum design of the forming tools in order to produce a desired part by multi-step stamping within a severe tolerance (0.1mm). The numerical method has been proposed to reduce the number of forming steps and therefore increasing the process productivity. To reach this goal, an integrated optimization approach, using the commercial finite element code ABAQUS© together with an optimization algorithm was developed. The optimization algorithm consists in constructing an explicit form of the objective function according to the design variables. To search the global optimum of the objective function, the SQP algorithm has been used. A thin metallic part formed by manual press and without blank-holder has been considered, to demonstrate the effectiveness of the optimization approach to get the optimal tools shape in a few iterations. [Copyright &y& Elsevier]

The main aim of this paper is to develop an algorithm for blending of multiple images in the image-stitching process. Our idea is to propose a variational method containing an energy functional to determine both a stitched image and weighting mask functions of multiple input images for image blending. The existence of the solution of the proposed energy functional is shown. We also present an alternative minimizing algorithm to solve the proposed model numerically and show the convergence of this algorithm. Experimental results show that the proposed model works effectively and efficiently and that the proposed method is competitive with the tested existing methods under noisy conditions. [ABSTRACT FROM AUTHOR]

A parallelized real-valued clonal selection algorithm (CLONALG) is successfully implemented in this paper utilizing message passing interface (MPI) to reduce the computational burden of a large clone pool. CLONALG is one of the many branches of Artificial Immune System (AIS) algorithms with unique inherent properties that make it a very efficient optimization techniques for multimodal problems such as the ones commonly encountered in computational electromagnetic design. As a demonstration of its effectiveness, a numerical study is carried out with known benchmark functions along with the optimization of multi-layered frequency selective surface (FSS) filters in the X-band. Our results show that the CLONALG can consistently outperform a standard GA implementation particularly in multi-modal optimization problems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate the convergence behavior of the Schwarz waveform relaxation (SWR) algorithms for solving PDEs with time delay. We choose the reaction diffusion equations with a constant time delay as the underlying model problem and try to derive optimized transmission conditions of Robin type. To this end, we propose a new method to get quasi-optimized parameter involved in the transmission conditions and it is shown that this method is essentially different from the existing ones. Moreover, when the situation is reduced into the heat equations with a constant delay, we show that this method results in a more efficient quasi-optimized parameter. Numerical results are provided to validate our theoretical results. [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]

In this paper, we employ selective bit mapping to improve the performance of the LDPC coded QAM scheme for terrestrial DTV broadcasting system. The threshold of message-passing decoding can be considerably lowered by selectively mapping the binary components of LDPC codeword to the positions in the m-tuples to be mapped into 2^mQAM symbols. In our approach, the mapping pattern is described by bit-mapping polynomials, based on which density evolution can be applied. The optimization algorithm is developed with two implementation concerns, using the Chinese DTMB standard as an example. Numerical results illustrate that our proposed approach can improve the decoding threshold by 0.05 dB to 0.499 dB depending on the code rate and the order of QAM modulation. Simulation results show that the actual BER improvement varies from 0.09 dB to 0.6 dB with different code-modulation combinations in both single-carrier and OFDM modes. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a new class of stochastic optimization algorithm called SOMA (self-organizing migrating algorithm) was proposed in the literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a “social environment”. This paper proposes a SOMA approach combined with a cultural algorithm (CSOMA) technique based on normative knowledge as an alternative method to solving the economic load dispatch problem of thermal generators with the valve-point effect. The classical SOMA and CSOMA approaches are validated for two test systems consisting of 13 and 40 thermal generators whose non-smooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the CSOMA present best results when compared with results of others optimization methods found in the literature in solving load dispatch problems with the valve-point effect. [ABSTRACT FROM AUTHOR]

Reservoir flood control operation (RFCO) is a large scale multiobjective problem with complex constraints that require powerful algorithms to solve it. As a new metaheuristic evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the potential ability to solve multiobjective optimization problems because of its group evolution characteristic. In this paper, we present a novel multiobjective shuffled frog leaping algorithm (MOSFLA), which incorporates an archiving strategy based on self-adaptive niche method to maintain the nondominated solutions, and improves the memetic evolution process of SFLA to adapt to the multiobjective optimization problem. The numerical experiments of five Zitzler-Deb-Thiele functions indicate that MOSFLA yields better-spread solutions and converges closer to the true Pareto frontier than non-denominated sorting genetic algorithm (NGSA)-II and SPEA2. Furthermore, MOSFLA is applied to solve RFCO of the Three Gorges Project, and the results demonstrate that this algorithm can generate a solution set with uniform spread and good convergence for the problems with two conflicting objectives, including minimizing the highest reservoir water level and minimizing the peak flood discharge. Additionally, if compared with dynamic programming and NGSA-II, MOSFLA is verified to be more efficient and competitive, and thus can be provided as a new effective alternative for solving the complex reservoir operation problems. [ABSTRACT FROM AUTHOR]

Abstract: An identification algorithm for a power system load model is proposed in this paper. The overall non-convex identification problem is separated into convex and non-convex subproblems, allowing for a global optimum to be found. Numerical experiments using data from both simulated and physical systems illustrate the accuracy of the proposed algorithm. Experiments are performed to investigate the robustness of the algorithm. [Copyright &y& Elsevier]

This paper presents a methodology for computing formally sensitivities of the magnetic moment method. This numerical method is known to be very accurate and light to compute force and torque acting on an open field problem. We propose an approach which computes formal derivatives with respect to the geometrical parameters of the device. Once the sensitivities are obtained, gradient based optimization algorithm can be used. [ABSTRACT FROM AUTHOR]