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Abstract: A generalization of two sharp inequalities in a recent paper by N. Ujević is established. Applications in numerical integration are also given and the results of N. Ujević are revised and improved. [Copyright &y& Elsevier]

Multi-product pipelines usually transport several products in batches to respective delivery stations. As for a multi-product pipeline with multiple pump stations, this paper develops a continuous-time mixed-integer linear programming (MILP) model based on flowrate database to optimize its detailed scheduling. In the proposed model, various unit pump cost and flowrate constraints, which strongly depend on pump operation schemes, are introduced for the economy and safety of solved scheduling plans. Moreover, this paper considers the actual field processing constraints which vary with batch interface migration and rarely considered in previous work. And a novel method of historical flowrate database preprocessing is presented to enhance solving efficiency. Finally, through comparing with three real-world cases solved by another two available models, the proposed one performs the best in scheduling optimization as well as substantial reduction of pump cost. [ABSTRACT FROM AUTHOR]

This paper presents an optimization model of inspection and replacement planning for a refinery plant under the consideration of corrosion in terms of cost. The management of corrosion is an essential task for processes that operate over several years without a shutdown. This is because corrosion can cause severe failures by thinning the wall thickness and eventually cause pipes or equipment to burst. However, required safety measures, such as the corrosion management, involve costly inspection and replacement. Therefore, a cost-effective safety-action strategy is proposed in this paper. The developed model presents an optimal combination of steel grade, design wall thickness, inspection number, and inspection timing under a given corrosion rate to minimize the cost of design, inspection, replacement, and failure. Three case studies using sensitivity analyses are applied to three major processes in a refinery plant: a crude distillation unit, visbreaker, and hydrocracker. [ABSTRACT FROM AUTHOR]

Let B be a set of b blue points and R be a set of r red points in the plane. In this paper we study the problem of finding rectangles that contain the maximum number of blue points without containing any red points, known as the maximum-box problem . First we study this problem for axis-aligned rectangles, and propose an exact worst-case optimal O ( r 2 + r b + b log ⁡ b ) time algorithm using O ( r + b ) space to find all maximum boxes. We also provide a 2-approximation algorithm running in O ( ( r + b ) log ⁡ ( r + b ) ) time and using O ( r + b ) space to find a single maximum box in the axis-aligned case. Then we generalize the exact algorithm for the axis-aligned case to find all arbitrarily oriented maximum boxes leading to a worst-case optimal O ( ( r + b ) 2 ( r + log ⁡ b ) ) time algorithm using O ( ( r + b ) 2 ) space to solve the problem. We conclude the paper by discussing time and space trade-offs. Our results improve the previously best known solutions to the maximum-box problem. [ABSTRACT FROM AUTHOR]

The paper deals with the obstacle-like minimization problem in the cylindrical domain Ω = D × (− l , l) J (u) = ∫ Ω | ∇ u | 2 d x + 2 ∫ D max { v (x ′) , 0 } d x ′ , where x = (x ′ , x n) , and v (x ′) = ∫ − l l u (x ′ , x n) d x n . The corresponding Euler–Lagrange equation is Δ u (x ′ , x n) = χ { v > 0 } (x ′) + − ∂ x n u (x ′ , − l) + ∂ x n u (x ′ , l) χ { v = 0 } (x ′). Due to the non-local nature of the obstacle, the comparison principle does not hold for the minimizers u (x) , which makes the problem challenging both analytically and numerically. The standard optimization techniques such as Newton or quasi-Newton's methods require approximations of the Jacobians that are four dimensional tensors and are prohibitively expensive both in storage and computational time due to the nature of the three dimensional problem. In this paper, a new algorithm that can compute the global minimum is introduced. Non-trivial exact solutions have been constructed; and second order accuracy has been confirmed. Another important contribution is the numerical testing of the comparison principle for functions v (x ′) , as conjectured by M. Chipot and the second author in Chipot and Mikayelyan (2022). [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to propose a new hybrid optimization method for the multiplicative noise and blur removal problem. A degraded image can often be recovered efficiently by minimizing an objective function which consists of a data-fidelity term and a regularization term. In the paper, we apply the quadratic penalty function method combined with the alternating direction method to minimize the corresponding objective function. Numerical experiments are presented to demonstrate the effectiveness of the proposed method. Experimental results illustrate the state-of-the-art performance of the proposed method to handle multiplicative noise and blur removal problem. [ABSTRACT FROM AUTHOR]

When heat transfer enhancing technology is applied to design heat transfer unit, the improvement of heat transfer will usually accompanied with a dramatically increase of flow resistance. The main objective of present paper is to propose an optimization design theory and develop a corresponding approach to design heat transfer unit, in which the heat transfer and flow resistance will be considered simultaneously. As an example, this paper shows how to obtain a high performance tube in convective heat transfer by optimizing the shape profile using the theory and approach. A 2-D model is used in a direct problem solver, which is solved by finite element software and provides the numerical results in the optimization. Power consumption of flow field P f is used to measure the flow resistance while generalized thermal resistance R h is used to measure the thermal resistance. Meanwhile, genetic algorithm (GA) and simplified conjugate-gradient method (SCGM) are applied in this study to optimize the objective function composed by these two aspects. Based on the results of numerical calculation, the optimal velocity field with 61.93% decrease in P f and 17.13% increase in R h is obtained. Subsequently, the performances at different inlet velocities V i are investigated. It is found that the shape profile obtained at V i = 0.1 m / s always has a better performance than the circular one, even if it is not the ideal shape. Finally, the further optimizations based on different V i and objective functions are discussed. The results prove that the proposed optimization method is effective in designing the shape of tube. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution. [Copyright &y& Elsevier]

Abstract: This paper presents a structured methodology for the constrained multi-objective optimization (MO) of a continuous cross-flow atomic layer deposition (ALD) reactor model with temporal precursor pulsing. The process model has been elaborated and experimentally validated in the first two papers of this series (Holmqvist et al., 2012, 2013). A general constrained MO problem (MOP) was formulated to simultaneously optimize quasi-steady-state reactor throughput and overall precursor conversion for the controlled deposition of ZnO films from and , subject to a set of operational constraints. These constraints included lower bounds for the cross-substrate film thickness uniformity and post-precursor purge duration. The non-dominated Pareto optimal solutions obtained successfully revealed the relation between the incommensurable process objectives and reduced the design space of the ALD process into a feasible set of design alternatives. The results presented here show that post-precursor purge duration is essential when optimizing throughput in temporally separated ALD processes, and that this is a major drawback when considering operation at atmospheric pressure. Finally, the robustness of the process along the Pareto optimal front, i.e. the ability of the process to accommodate variations in the associated set of optimal decision variables (DVs), was assessed by Monte Carlo simulations, in which the values of the parametric uncertainties were randomly generated from a multivariate normal distribution. The uncertainty and sensitivity analysis showed that the inherent robustness of the process is progressively lost with the precursor conversion, and revealed the mechanistic dependence of all DVs on the proposed optimization specifications. [Copyright &y& Elsevier]

Abstract: Shape optimization in connection with numerical modelling is used to reduce bending and associated flexural stresses in buried concrete arches. Modelling of the arch is carried out via a nonlinear finite element model that accounts for soil constitutive relations, soil–structure interactions, sequential construction stages and soil compaction. Centre line of the arch is parameterized by Bézier curve with three degrees of freedom that are subjected to optimization by genetic and Levenberg–Marquardt algorithm. The paper presents a parametric study which aims to determine the optimal shapes for buried arches of various span/rise ratios, backfill depths and foundation soil types. In the second part of the paper it shows a theoretical reduction in tensile stresses obtained by shape optimization of concrete arch culvert with a 9.4m span tested at the University of Massachusetts at Amherst. [Copyright &y& Elsevier]

Abstract: Upper and lower bound theorems of limit analyses have been presented in part I of the paper. Part II starts with the finite element discretization of these theorems and demonstrates how both can be combined in a primal–dual optimization problem. This recently proposed numerical method is used to guide the development of a new class of closed-form limit loads for circumferential defects, which show that only large defects contribute to plastic collapse with a rapid loss of strength with increasing crack sizes. The formulae are compared with primal–dual FEM limit analyses and with burst tests. Even closer predictions are obtained with iterative limit load solutions for the von Mises yield function and for the Tresca yield function. Pressure loading of the faces of interior cracks in thick pipes reduces the collapse load of circumferential defects more than for axial flaws. Axial defects have been treated in part I of the paper. [Copyright &y& Elsevier]

Abstract: This paper presents dynamic output feedback model predictive control (DOFMPC) for nonlinear systems represented by a Hammerstein–Wiener model. Compared with a previous work (IET-OFMPC: output feedback model predictive control for nonlinear systems represented by Hammerstein–Wiener model. IET Control Theory & Applications, 2007, 1 (5) pp. 1302–1310), this paper uses the notion of quadratic boundedness to specify the closed-loop stability and guarantees the recursive feasibility of the optimization problems. By optimizing all the parameters of the dynamic output feedback law within a single optimization problem, the computational burden is very huge. Hence, an alternative formulation is also proposed with much lower on-line computational burden. Numerical examples are given to illustrate the effectiveness of the controllers. [Copyright &y& Elsevier]

Abstract: Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks. [Copyright &y& Elsevier]

Abstract: The aims of multi-objective optimization are (1) to find pareto-optimal solutions and (2) to analyze the trade-off between conflicting objectives. This paper proposes an interactive method for solving multi-objective optimization problems using the satisficing trade-off method (STOM). In particular, we introduce a trade-off matrix to quantitatively analyze the trade-off between conflicting objectives. Each element of the trade-off matrix consists of a projection matrix of active constraints and gradients of objective functions. In addition, the compromise point and the compromise solution incorporating the trade-off ratio that the decision-maker desires are defined in this paper. The technique to obtain the compromise point is proposed in this paper. Through numerical examples, the validity proposed method is examined. [Copyright &y& Elsevier]

Abstract: With the development of dry high vacuum pump, researches of pumping mechanism of vortex-stage are greatly concerned. This paper presents a horizontal dry high vacuum pump and establishes a numerical model of vortex stage. And then numerical simulation of flow is carried out with FLUENT software. Moreover, it studies how flow regions work on the internal flow and work performance of the vortex stage under various conditions, such as different number of blades and impeller with different blade rake. As a result, numerical simulation shows that there is a large impact on the pumping for different numbers of blades distributed on the impeller, the number of blades of single impeller should be obtained by combining with practical design sizes. In fact, this paper selects the best number of blades as forty-three by calculating and optimizing. In the mean time, there are three cases for the blade rake: pitched vanes, radial vanes and retroverted vanes. For each case, there are both longitudinal vortex and radial vortex existing in the impeller. Considering comprehensively, impeller with radial vanes is selected in the design after simulation and comparisons. [Copyright &y& Elsevier]

Abstract: This paper uses Diffuser Augmented Wind Turbine for reference. According to the characteristics of ocean current movement, this paper gives several dome model programs for the straight blade vertical-axis tidal turbine. Make use of it to increase its output power. This paper gives several dome model programs for the straight blade vertical-axis tidal turbine. And optimum scheme is chosen by numerical simulation. [Copyright &y& Elsevier]

Highlights • The cross Gramian-based reduction for the discrete systems is proposed. • The H 2 norm of the discrete system is expressed by the cross Gramian. • The constraint conditions of the H 2 optimal problem is further relaxed. • The product manifold is used to reduce the discrete system. Abstract This paper studies the H 2 optimal model order reduction of the discrete system based on the cross Gramian, which equips both the controllability and observability information. The cross Gramian is first employed to obtain the H 2 norm of the discrete systems. Then, the constrained conditions of the H 2 optimal model order reduction problem of the discrete systems are relaxed, and a new H 2 optimal model order reduction problem on the product manifold is reformulated. Finally, geometry of the resultant optimization problem is investigated and an iterative algorithm is presented. The efficiency of the proposed algorithm is demonstrated by a practical example. [ABSTRACT FROM AUTHOR]

Abstract The spectral conjugate gradient method is an effective method for large-scale unconstrained optimization problems. In this paper, based on Quasi-Newton direction and Quasi-Newton equation, a new spectral conjugate gradient method is proposed. This method is motivated by the three-term modified Polak-Ribiyre-Polyak (PRP) method and spectral parameters. The global convergence of algorithm is proved for general functions under a strong Wolfe line search. Numerical results show that the new algorithm is superior to the three-term modified PRP method. [ABSTRACT FROM AUTHOR]

Abstract Recently, the l p -norm regularization minimization problem (P p λ) has attracted great attention in compressed sensing. However, the l p -norm ‖x‖ p p in problem (P p λ) is nonconvex and non-Lipschitz for all p ∈ (0 , 1) , and there are not many optimization theories and methods proposed to solve this problem. In fact, it is NP-hard for all p ∈ (0 , 1) and λ > 0. In this paper, we study one modified l p -norm regularization minimization problem to approximate the NP-hard problem (P p λ). Inspired by the good performance of Half algorithm in some sparse signal recovery problems, an iterative thresholding algorithm is proposed to solve our modified l p -norm regularization minimization problem (P p , 1 ∕ 2 , ϵ λ). Numerical results on some sparse signal recovery problems show that our algorithm performs effectively in finding the sparse signals compared with some state-of-art methods. [ABSTRACT FROM AUTHOR]

Abstract Time-varying nonlinear optimization (TVNO) problems are considered as important issues in various scientific disciplines and industrial applications. In this paper, the continuous-time derivative dynamics (CTDD) model is developed for obtaining the real-time solutions of TVNO problems. Furthermore, aiming to remedy the weaknesses of CTDD model, a continuous-time zeroing dynamics (CTZD) model is presented and investigated. For potential digital hardware realization, by using bilinear transform, a general four-step Zhang et al discretization (ZeaD) formula is proposed and applied to the discretization of both CTDD and CTZD models. A general four-step discrete-time derivative dynamics (general four-step DTDD) model and a general four-step discrete-time zeroing dynamics (general four-step DTZD) model are proposed on the basis of this general four-step ZeaD formula. Further theoretical analyses indicate that the general four-step DTZD model is zero-stable, consistent and convergent with the truncation error of O (g 4) , which denotes a vector with every entries being O (g 4) with g denoting the sampling period. Theoretical analyses also indicate that the maximal steady-state residual error (MSSRE) has an O (g 4) pattern confirmedly. The efficacy and accuracy of the general four-step DTDD and DTZD models are further illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a way to generate a robot genome that contributes to defining the personality of a software robot or an artificial life in a mobile phone. The personality should be both complex and feature-rich, but still plausible by human standards for an emotional life form. However, it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot''s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes a neural network algorithm for a genetic robot''s personality (NNGRP) and an upgraded version of a previously introduced evolutionary algorithm for a genetic robot''s personality (EAGRP). The robot genomes for heterogeneous personalities are demonstrably generated via the NNGRP and the EAGRP and compared. The implementation is embedded into genetic robots in a mobile phone to verify the feasibility and effectiveness of each algorithm. [Copyright &y& Elsevier]

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]

Abstract: Pollution emission reduction is becoming an inevitable global goal. Incorporating pollution reduction goals into power system operation affects several different aspects, such as unit scheduling and system reliability. At the same time, changes in the energy scheduling change the required optimal reserve amount. Optimal spinning reserve scheduling also affects the energy market scheduling. Optimal reserve allocation changes the energy scheduling, which affect the amount of pollution emission. Therefore, incorporating pollution emission reduction and optimal spinning reserve scheduling cannot be studied separately. Analysis of the system effects of pollution reduction should be performed considering the ancillary service market, specificity the optimal spinning reserve scheduling. This problem is addressed in this paper by incorporating optimal spinning reserve scheduling in a combined environment economic dispatch (CEED) in one objective function. The framework of this paper enables the study of the effect of optimal reserve scheduling and emission reduction as well as an analysis of the system effects of pollution reduction. With the increased AMI and smart grid realization, the reserve supplying demand response (RSDR) is becoming an important player in the reserve market, and thus, these resources are also taken into account. In this paper, the objective function is social cost minimization, including the costs associated with energy provision, reserve procurement, expected interruptions and environmental pollution. A MIP-based optimization method is developed, which reduces the computational burden considerably while maintaining the ability to reach to the optimal solution. The IEEE RTS 1996 is used as a test case for numerical simulations, and the results are presented. The numerical results show that optimal reserve scheduling and RSDR utilization resources have a considerable impact on environmental–economic cost characteristics. [Copyright &y& Elsevier]

Abstract: Computing traffic equilibria with a general nonadditive route cost disutility function is considered in this paper. Following the user equilibrium (UE) condition, that is, no driver can unilaterally change route to achieve less travel costs, the traffic equilibrium problem (TEP) can be formulated as a nonlinear complementary problem (NCP). In this paper, we propose a semismooth Newton method with a penalized Fischer–Burmeister (PFB) NCP function to solve the NCP formulation of the TEP, and also, we investigate the properties of the proposed method. Numerical results are provided and compared with the classical TEP with additive route cost functions. The results show the algorithm can achieved substantially better performance than the existing approaches. A sensitivity analysis is also conducted to examine the parameter of the proposed nonadditive route cost function. [Copyright &y& Elsevier]

Abstract: This paper introduces a modified power flow model for Hybrid Flow Controller (HFC) as an energy flow controller. The existing power flow models for Hybrid Flow Controller are suitable only for conventional power flow analysis, and are not applicable for OPF and optimal location analysis of FACTS devices. In this paper, some modifications were applied to the existing models to promote the accuracy and improve their conformability on any power system and hence leading to a precise steady-state analysis. The modified model and the existing model are investigated using different IEEE test systems and the results are compared together. The optimization method is numerically solved using Matlab and General Algebraic Modelling System (GAMS) software environments. The solution procedure uses Mixed Integer Non-Linear Programming (MINLP) and Relaxed Mixed Integer Non-Linear Programming (RMINLP) to solve the optimal location and setting of HFC incorporated in OPF problem considering the total fuel cost, power losses, and the system loadability as objective functions for single objective optimization problem and improve the power system operation. [Copyright &y& Elsevier]

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

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

Abstract: In this paper, we consider minimizing multiple linear objective functions under a max--norm fuzzy relational equation constraint. Since the feasible domain of a max–Archimedean -norm relational equation constraint is generally nonconvex, traditional mathematical programming techniques may have difficulty in yielding efficient solutions for such problems. In this paper, we apply the two-phase approach, utilizing the min operator and the average operator to aggregate those objectives, to yield an efficient solution. A numerical example is provided to illustrate the procedure. [Copyright &y& Elsevier]

This paper focuses on a multi-planar steel tubular joint, which is in the slope change position of a new transmission tower. This slope change joint (SCJ) had six branch tubes and five chords in total, which led to complex interaction in the joint behaviour. To achieve experimental loading, a three-dimensional setup was designed for this joint. Detailed information of this SCJ and the test equipment were introduced. Then numerical model was established and validated by test results. Stress conditions of primary members, failure modes, influencing factors of the ultimate load as well as load transmission path were investigated by taking advantage of numerical analysis. Furthermore, a criterion for joint capacity was established referring to steel structure standards in different countries. Based on the established criterion, critical members were revealed and strengthened. In the end, procedures to optimize critical members and recommendations for joint design were proposed. Results proved that this SCJ can well meet the strength requirements after optimization. [ABSTRACT FROM AUTHOR]

This paper presents a topology optimization method for the sequential design of material layout and fiber orientation in functionally graded fiber-reinforced composite structures. Specifically, the proposed method can find the optimal structural layout of matrix and fiber materials together with optimal discrete fiber orientations. In this method, an orientation design variable in the Cartesian coordinate system is employed with a conventional density design variable. The orientation design variable controls not only the fiber orientation, but also fiber volume fraction. The fiber volume fraction control can be used to relax the orientation design problem and simultaneously design a functionally graded structural layout of fiber material. To avoid intermediate fiber orientations and achieve discrete fiber orientation design, a penalization scheme is applied to the orientation design variable. For solving the optimization problem which involves multiple design variables such as the density variable, fiber orientation variable, and target discrete orientation set, a three-step sequential optimization procedure is proposed. In this procedure, the result for each step provides the isotropic design, continuous fiber orientation design, and functionally graded discrete orientation design, respectively. To validate the effectiveness of the proposed approach, numerical examples for structural compliance minimization and compliant mechanism design are provided. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

This paper presents an improved scheme for the state-of-health (SOH) estimation, applicable to battery management system (BMS) of electric vehicles. The original scheme requires the prior information for the estimation, which is the prior SOH identified from the last charging. This limit implies that if a battery or its BMS is replaced, the prior SOH stored within the BMS no longer matches with the actual SOH, resulting in a critical error. To avoid this potential but critical pitfall, we newly devise an improved SOH estimation scheme. The original scheme is revised by adopting the fixed-point iteration method into its parameter estimation. By removing dependencies on the prior information, the revised scheme can function regardless of such replacements. The revised scheme is experimentally validated and demonstrated that even without the prior information, it can satisfy the requirement of the SOH estimation (within 3%) thanks to its improved design. [ABSTRACT FROM AUTHOR]

In this paper, we consider near-optimal operation for a class of unconstrained batch processes using the self-optimizing control (SOC) methodology. The existing static SOC approach is extended to the dynamic case by means of a static reformulation of the dynamic optimization problem. However, the dynamic SOC problem is posed as a structure-constrained controlled variable (CV) selection problem, which is different from the static cases. A lower-block triangular structure is specified for the combination matrix, H , to allow for optimal operation whilst respecting causality. A new result is that the structure-constrained SOC problem still results in a convex formulation, which has an analytic solution where the optimal CVs associated with discrete time instants are solved separately. In addition, the inputs are directly determined based on current CV functions for on-line utilization. A fed-batch reactor and a batch distillation column are used to demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel approach to improve prediction accuracy of GM(1,1) model through optimization of the initial condition in this paper. The new initial condition is comprised of the first item and the last item of a sequence generated from applying the first-order accumulative generation operator on the sequence of raw data. Weighted coefficients of the first item and the last item in the combination as the initial condition are derived from a method of minimizing error summation of square. We can actually find that the newly modified GM(1,1) model is an extension of the original GM(1,1) model and another modified model which takes the last item in the generated sequence as the initial condition when weighted coefficients takes distinctly specific values. The new optimized initial condition can express the principle of new information priority emphasized on in grey systems theory fully. The result of a numerical example indicates that the modified GM(1,1) model presented in this paper can obtain a better prediction performance than that from the original GM(1,1) model. [Copyright &y& Elsevier]

Abstract: In this paper, we deal with conjugate gradient methods for solving nonlinear least squares problems. Several Newton-like methods have been studied for solving nonlinear least squares problems, which include the Gauss–Newton method, the Levenberg–Marquardt method and the structured quasi-Newton methods. On the other hand, conjugate gradient methods are appealing for general large-scale nonlinear optimization problems. By combining the structured secant condition and the idea of Dai and Liao (2001) , the present paper proposes conjugate gradient methods that make use of the structure of the Hessian of the objective function of nonlinear least squares problems. The proposed methods are shown to be globally convergent under some assumptions. Finally, some numerical results are given. [Copyright &y& Elsevier]

Abstract: A temporal finite element discretization of a boundary value problem has several advantages compared to a time-integrating evolution form for optimized target movement simulations. The paper gives some basic aspects on how such a finite element form can be stated, with both displacements and controls discretized and seen as unknowns. Aspects on the resulting formulations are discussed. Important issues are the order, continuity and fineness of the discretizations. When the formulation is seen in an optimization context, minimizing the effort for a prescribed movement, the discretization affects the results obtained in several manners, where some aspects of results are artifacts. The paper discusses these effects from basic principles, but also verifies them in numerical simulations. [Copyright &y& Elsevier]

Abstract: This paper discusses stabilization of an optimization procedure to determine an appropriate semivariogram parameter using the empirical semivariogram for Kriging-based approximation. From a viewpoint of a computational cost for constructing a surrogate model, a semivariogram fitting approach using the empirical semivariogram can be usable for the parameter estimation of a semivariogram function. However, instability of the optimization procedure for determination of the semivariogram parameter may be observed in some cases and it causes to generate an invalid surrogate model. For this problem, a simple technique for stabilization of the optimization for the parameter determination is proposed in this paper. The proposed approach employs a normalization technique of input data with respect to values of variables and an objective function. The proposed method is applied to some numerical examples, and the numerical results illustrate validity and effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: The m-machine open shop problem to minimize the sum of the completion times is investigated in our paper. First, a heuristic algorithm, Shortest Processing Time Block, is presented to deal with problem , where k is positive integer. And then, the heuristic is extended to solve the general problem . It is proved that the heuristic is asymptotically optimal when the job number goes to infinity. At the end of this paper, numerical experimentation results show the effectiveness of the heuristic algorithm. [Copyright &y& Elsevier]

Abstract: In this paper we study a mean field model for discrete time, finite number of states, dynamic games. These models arise in situations that involve a very large number of agents moving from state to state according to certain optimality criteria. The mean field approach for optimal control and differential games was introduced by Lasry and Lions (2006, 2007) . The discrete time, finite state space setting is motivated both by its independent interest as well as by numerical analysis questions which appear in the discretization of the problems introduced by Lasry and Lions. The main contribution of this paper is the exponential convergence to equilibrium of the initial-terminal value problem. [Copyright &y& Elsevier]

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: Nowadays, the search in reliability-based design optimization is becoming an important engineering design activity. Traditionally for these problems, the objective function is to minimize a cost function while satisfying the reliability constraints. The reliability constraints are usually formulated as constraints on the probability of failure. This paper focuses on the study of a particular problem with the failure mode on vibration of structure. The difficulty in evaluating reliability constraints comes from the fact that modern reliability analysis methods are themselves formulated as an optimization problem. Solving such nested optimization problems is extremely expensive for large-scale multidisciplinary systems which are likewise computationally intensive. With this in mind research, we propose in this paper a new method to treat reliability-based optimization methods under frequencies constraint. The goal of this development has resolved just one problem of optimization and reduced the cost of computation. Aircraft wing design typically involves multiple disciplines such as aerodynamics and structure; this numerical example demonstrated the different advantages of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents a numerical study on laminar forced convection of water in offset strip-fin microchannels network heat sinks for microelectronic cooling. A 3-dimensional mathematical model, consisting of N–S equations and energy conservation equation, with the conjugate heat transfer between the heat sink base and liquid coolant taken into consideration is solved numerically. The heat transfer and fluid flow characteristics in offset strip-fin microchannels heat sinks are analyzed and the heat transfer enhancement mechanism is discussed. Effects of geometric size of strip-fin on the heat sink performance are investigated. It is found that there is an optimal strip-fin size to minimize the pressure drop or pumping power on the constraint condition of maximum wall temperature, and this optimal size depends on the input heat flux and the maximum wall temperature. The results of this paper are helpful to the design and optimization of offset strip-fin microchannel heat sinks for microelectronic cooling. [Copyright &y& Elsevier]

Abstract: The VIKOR method was developed for multi-criteria optimization of complex systems. It determines the compromise ranking list and the compromise solution obtained with the initial (given) weights. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. It introduces the multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. The aim of this paper is to extend the VIKOR method for decision making problems with interval number. The extended VIKOR method’s ranking is obtained through comparison of interval numbers and for doing the comparisons between intervals, we introduce α as optimism level of decision maker. Finally, a numerical example illustrates and clarifies the main results developed in this paper. [Copyright &y& Elsevier]

Abstract: The focus of this paper is the optimization of complex multi-parameter systems. We consider systems in which the objective function is not known explicitly, and can only be evaluated through computationally intensive numerical simulation or through costly physical experiments. The objective function may also contain many local extrema which may be of interest. Given objective function values at a scattered set of parameter values, we develop a response surface model that can dramatically reduce the required computation time for parameter optimization runs. The response surface model is developed using radial basis functions, producing a model whose objective function values match those of the original system at all sampled data points. Interpolation to any other point is easily accomplished and generates a model which represents the system over the entire parameter space. This paper presents the details of the use of radial basis functions to transform scattered data points, obtained from a complex continuum mechanics simulation of explosive materials, into a response surface model of a function over the given parameter space. Response surface methodology and radial basis functions are discussed in general and are applied to a global optimization problem for an explosive oil well penetrator. [Copyright &y& Elsevier]

Abstract: According to the characteristic of modern product and optimization design, the conception and content of parameterized finite element analysis (PFEA) are introduced in this paper. Based on the request of PFEA, the conception of parameterized meshing is presented. The approach of establishing finite element models by parameterized meshing is elaborated. The content and realization method of parameterized meshing are researched in this thesis. Simultaneously, the process to realize parameterized meshing on Unigraphics (UG) software is proposed. And by programming, ParaMesh system—the parameterized meshing system based on history is developed for the first time in this paper, and the parameterized meshing for the surface is realized. At last, several examples on parameterized meshing are provided. [Copyright &y& Elsevier]

Abstract A common approach to solving global optimization problems is to use univariate optimization algorithms in combination with dimensional reduction schemes. The paper considers five types of Peano-like space-filling curves (evolvents), which are used to reduce the dimension in the derivative-free algorithm of global optimization. The algorithm is univariate and developed within the framework of the information-statistical approach. This work is the first one, where convergence rates and implementations details of these five evolvents are considered together and directly compared. [ABSTRACT FROM AUTHOR]

In this paper, we significantly extend the applicability of state-of-the-art ELDP (equations for linearizing discrete product terms) method by providing a new linearization to handle more complicated non-linear terms involving both of discrete and bounded continuous variables. A general class of “representable programming problems” is formally proposed for a much wider range of engineering applications. Moreover, by exploiting the logarithmic feature embedded in the discrete structure, we present an enhanced linear reformulation model which requires half an order fewer equations than the original ELDP. Computational experiments on various engineering design problems support the superior computational efficiency of the proposed linearization reformulation in solving engineering optimization problems with discrete and bounded continuous variables. [ABSTRACT FROM AUTHOR]