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Abstract: The aim of this note is to show that the optimization algorithm proposed in may not lead to the optimal solution in some cases. In fact, the optimization problem remains open if we do not apply the branch-and-bound method to solve it or compute all the objective values of minimal solutions of the feasible domain. [Copyright &y& Elsevier]

In this paper we discuss scenario reduction methods for risk-averse stochastic optimization problems. Scenario reduction techniques have received some attention in the literature and are used by practitioners, as such methods allow for an approximation of the random variables in the problem with a moderate number of scenarios, which in turn make the optimization problem easier to solve. The majority of works for scenario reduction are designed for classical risk-neutral stochastic optimization problems; however, it is intuitive that in the risk-averse case one is more concerned with scenarios that correspond to high cost. By building upon the notion of effective scenarios recently introduced in the literature, we formalize that intuitive idea and propose a scenario reduction technique for stochastic optimization problems where the objective function is a Conditional Value-at-Risk. Numerical results presented with problems from the literature illustrate the performance of the method and indicate the cases where we expect it to perform well. [ABSTRACT FROM AUTHOR]

Bat algorithm (BA) is a new approach designed by imitating bat's behavior of searching and capturing preys. The existing results have demonstrated the effectiveness and efficiency in comparison with other heuristic algorithms such as genetic algorithms and particle swarm optimization. In this paper, we design a novel framework for bat algorithm named two-stage bat algorithm (TSBA) using a trade-off strategy which balances the relationship between exploration and exploitation at the most extent. Inspired by the multi-population methods (e.g., artificial bee colony), we not only concern some technologies to avoid premature inevitably encountered when using BA, but also use a trade-off strategy to improve the comprehensive search performance for optimization. Some typical test sets which consist of 27 benchmark functions are utilized in comparative experiment, and the simulation results in terms of convergence rate and accuracy illustrate that the TSBA has a competitive performance than other swarm intelligent optimization algorithms. In addition, the proposed algorithm will not lend to the tremendous increase in computing time and thus will be a powerful tool in practical applications. [ABSTRACT FROM AUTHOR]

In design optimization problems, the robustness of a solution is essential to be considered when it is going to be implemented or used in real world. Robustness can be defined in different ways, but in this work, it is considered as the property of a solution’s performance to be as insensitive as possible to perturbations in that solution. Optimizing both quality and robustness is generally addressed as a multiobjective problem. This paper presents a complete framework aided by decision maker, which can be used to solve robust optimization problems with one or more objectives. Decision maker actively takes part of the process, defining a priori the robustness metric, and specifying iteratively a robustness factor that joins quality and robustness metrics into a single fitness function. The framework is very flexible allowing decision maker not only to specify the right robustness level for him, but also to exchange metrics and algorithms if he wants. The framework was tested and validated using two single-objective robust benchmarking functions, and in one new multiobjective robust function, created to challenge the framework, which tries to seek the most robust Pareto optimal front according to decision maker preferences. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new predictor-corrector interior-point algorithm for linear programming based on a wide neighbourhood. In each iteration, the algorithm computes the Ai-Zhang's predictor direction (SIAM J. Optim. 16(2):400-417, ) and a new corrector direction, in an attempt to improve its performance. We drive that the duality gap reduces in both predictor and corrector steps. Moreover, we also prove that the complexity of the algorithm coincides with the best iteration bound for small neighbourhood algorithms. Finally, some numerical experiments are provided which reveal capability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new in-plane XYθz nano-positioning stage that utilizes piezoelectric actuators and flexure mechanisms. The proposed stage has an aperture and is compact, which facilitates its application in measurement equipment, especially those used for biological specimens. The stage has four piezoelectric actuators and four bridge-type flexure mechanisms, which are used to amplify the small motions produced by the piezoelectric actuators. This paper describes the modeling and design optimization of the stage, which has X- and Y-direction motion ranges of 300 μm and a θz-direction motion range of ±3.9 mrad. The stage measures 150 × 150 × 23 mm, and its aperture is 50 × 50 mm. [ABSTRACT FROM AUTHOR]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

Abstract: The problem of layout and shape optimization of stiffeners in plates loaded in plane and in bending Kirchhoff’s plates is considered in the paper. Two types of reinforcement are analyzed here, namely introduction of elements made of stiffer material like fibers or beams and introduction of ribs. The problem of minimization (maximization) of arbitrary objective functional of displacements, strains, stresses or reactions with constraint imposed on the structure cost is considered. Using adjoint method, expressions for sensitivities with respect to introduction of stiffeners are derived and on this basis conditions of modification are formulated. In order to find optimal reinforcement, heuristic algorithm is proposed in the paper. At first, using information from sensitivity analysis, initial localization of a new fiber or rib is determined. Next, in order to correct their positions and to determine other parameters characterizing stiffened structure standard optimization is performed. Usually, optimal position of the stiffener only a little differs from the initial position. Numerical examples illustrate applicability of the method. [Copyright &y& Elsevier]

Abstract: This paper proposes a hybrid methodology that exploits strengths of wavelet analysis and support vector machine model in forecasting time series, and deals with the application of proposed methodology in manufacturing time series forecasting. This method is characteristic of the preprocessing of sample data using wavelet transformation for forecast, i.e., the data sequence of evolvement of share of some sectors in manufacturing is first mapped into several time-frequency domains, and then a support vector machine is established for each domain. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective support vector machine models corresponding to different time-frequency domains. Nevertheless, one of disadvantages of the method is dilemma of selection of values of parameters in support vector machine because the way of selecting values for the parameters will affect the generalization performance remarkably. In this paper, chaos optimization is applied to accomplish selection of values of parameters. Results of experiments based on gross values of textile product in Japan suggest that this hybrid method can both achieve higher accuracy in manufacturing forecasting. [Copyright &y& Elsevier]

Recent author's papers have shown new opportunities resulting from the treatment of resource planning in project scheduling as the optimization problem for a hybrid system. This approach gives the possibility to work out the optimum resource sharing in an iteration process of branch-and-bound type. The present paper concentrates on the most standard case of the problem in question for which all the relationships may be represented in the linear form. Two exact finite methods are proposed. The first method is obtained using the piecewise-linear form of Bellman function, the second evolves from the decomposition approach for dynamic linear programming problem. [ABSTRACT FROM AUTHOR]

Active Appearance Models (AAMs) are generative, parametric models that have been successfully used in the past to model deformable objects such as human faces. The original AAMs formulation was 2D, but they have recently been extended to include a 3D shape model. A variety of single-view algorithms exist for fitting and constructing 3D AAMs but one area that has not been studied is multi-view algorithms. In this paper we present multi-view algorithms for both fitting and constructing 3D AAMs. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. In the first part of the paper we describe an algorithm for fitting a single AAM to multiple images, captured simultaneously by cameras with arbitrary locations, rotations, and response functions. This algorithm uses the scaled orthographic imaging model used by previous authors, and in the process of fitting computes, or calibrates, the scaled orthographic camera matrices. In the second part of the paper we describe an extension of this algorithm to calibrate weak perspective (or full perspective) camera models for each of the cameras. In essence, we use the human face as a (non-rigid) calibration grid. We demonstrate that the performance of this algorithm is roughly comparable to a standard algorithm using a calibration grid. In the third part of the paper, we show how camera calibration improves the performance of AAM fitting. A variety of non-rigid structure-from-motion algorithms, both single-view and multi-view, have been proposed that can be used to construct the corresponding 3D non-rigid shape models of a 2D AAM. In the final part of the paper, we show that constructing a 3D face model using non-rigid structure-from-motion suffers from the Bas-Relief ambiguity and may result in a “scaled” (stretched/compressed) model. We outline a robust non-rigid motion-stereo algorithm for calibrated multi-view 3D AAM construction and show how using calibrated multi-view motion-stereo can eliminate the Bas-Relief ambiguity and yield face models with higher 3D fidelity. [ABSTRACT FROM AUTHOR]

This short correspondence is a reply to a recently published paper by Verlinde et al. in which the authors empirically compared fuzzy and nonfuzzy association analysis and, on the basis of their results, questioned the usefulness of a fuzzy approach. Although we highly welcome the critical examination of the topic and definitely agree that fuzzy extensions of existing methods call for a thorough justification, the empirical comparison presented in the aforementioned paper is in our opinion not objective and extensive enough to fully warrant the conclusions drawn from the results. Apart from some general comments on the claims raised in their paper, we present empirical results based on an alternative experimental setup that lead to different conclusions. [ABSTRACT FROM AUTHOR]

This paper deals with a problem of guaranteed (robust) financial decision-making under model uncertainty. An efficient method is proposed for determining optimal robust portfolios of risky financial instruments in the presence of ambiguity (uncertainty) on the probabilistic model of the returns. Specifically, it is assumed that a nominal discrete return distribution is given, while the true distribution is only known to lie within a distance d from the nominal one, where the distance is measured according to the Kullback-Leibler divergence. The goal in this setting is to compute portfolios that are worst-case optimal in the mean-risk sense, that is, to determine portfolios that minimize the maximum with respect to all the allowable distributions of a weighted risk-mean objective. The analysis in the paper considers both the standard variance measure of risk and the absolute deviation measure. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the study of necessary and sufficient optimality conditions for convex–concave fractional disjunctive programming problems for which the decision set is the union of a family of convex sets. The Lagrangian function for such problems is defined and the Kuhn–Tucker saddle and stationary points are characterized. In addition, some important theorems related to the Kuhn–Tucker problem for saddle and stationary points are established. Moreover, a general dual problem is formulated, and weak, strong and converse duality theorems are proved. Throughout the presented paper illustrative examples are given to clarify and implement the developed theory. [Copyright &y& Elsevier]

In this paper we use the penalty approach to study three constrained minimization problems. A penalty function is said to have the exact penalty property [J.-B. Hiriart-Urruty and C. Lemarechal, Convex Analysis and Minimization Algorithms, 2 vols., Springer-Verlag, Berlin, 1993] if there exists a penalty coefficient for which a solution of an unconstrained penalized problem is a solution of the corresponding constrained problem. In this paper we establish a very simple sufficient condition for the exact penalty property. [ABSTRACT FROM AUTHOR]

This paper presents a new verified optimization method for the problem of finding the densest packings of nonoverlapping equal circles in a square. In order to provide reliable numerical results, the developed algorithm is based on interval analysis. As one of the most efficient parts of the algorithm, an interval-based version of a previous elimination procedure is introduced. This method represents the remaining areas still of interest as polygons fully calculated in a reliable way. Currently the most promising strategy of finding optimal circle packing configurations is to partition the original problem into subproblems. Still, as a result of the highly increasing number of subproblems, earlier computer-aided methods were not able to solve problem instances where the number of circles was greater than 27. The present paper provides a carefully developed technique resolving this difficulty by eliminating large groups of subproblems together. As a demonstration of the capabilities of the new algorithm the problems of packing 28, 29, and 30 circles were solved within very tight tolerance values. Our verified procedure decreased the uncertainty in the location of the optimal packings by more than 700 orders of magnitude in all cases. [ABSTRACT FROM AUTHOR]

Abstract: The strong Rabin number of a network of connectivity is the minimum so that for any nodes , of , there exist node-disjoint paths from to , respectively, whose maximal length is not greater than , where and are not necessarily distinct. In this paper, we show that the strong Rabin number of a -dimensional folded hypercube is , where is the diameter of the -dimensional folded hypercube. Each node-disjoint path we obtain has length not greater than the distance between the two end nodes plus two. This paper solves an open problem raised by Liaw and Chang. [Copyright &y& Elsevier]

The focus of this overview for this issue of Nuclear Technology, which contains papers presented at the American Nuclear Society Advances in Nuclear Fuel Management III (ANFM-III) 2004 topical meeting, is to introduce the subject of nuclear fuel management for light water reactors. A total of 23 papers was presented on this topic at ANFM-III. Nuclear fuel management involves making the so-called out-of-core and in-core decisions. Simply put, the out-of-core decisions address the attributes of the new (fresh) fuel that will be fabricated and the partially burnt (shuffled) fuel to reinsert into the core for additional energy production. The in-core decisions address where the fresh and burnt fuel along with burnable poisons should be located in the core. The above applies to batch refueling strategies, e.g., pressurized water reactors and boiling water reactors (BWRs). For BWRs, additional in-core decisions enter to address control rod pattern paired with core flow rate as a function of burnup. It is obvious that the out-of-core and in-core decisions are coupled. The objective of nuclear fuel management is to minimize the cost of electrical energy generation subject to operational and safety constraints. Since fuel resides in the core for several cycles, a multicycle assessment is required to make nuclear fuel management decisions. For nearly four decades there has been an effort to develop automated computational capability to assist the reload core nuclear design engineer in making nuclear fuel management decisions. This development has ranged from employment of heuristic rules to utilization of mathematical optimization approaches. This overview reviews the development of nuclear fuel management optimization capabilities by first defining the problem, then describing current capabilities, and finally projecting where future capabilities need to be developed to support the needs of reload core nuclear design engineers. [ABSTRACT FROM AUTHOR]

This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle swarm optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into PSO on the basis of the proximate optimality principle (POP). In this paper, we propose adaptive PSO and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 41–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20077 [ABSTRACT FROM AUTHOR]

The condition-based approach for consensus solvability consists of identifying sets of input vectors, called conditions, for which there exists an asynchronous protocol solving consensus despite the occurrence of up to f process crashes. This paper investigates $\mathcal{C}_f$ , the largest set of conditions which allow us to solve the consensus problem in an asynchronous shared memory system.The first part of the paper shows that $\mathcal{C}_f$ is made up of a hierarchy of classes of conditions, $\mathcal{C}^{[d]}_f$ where d is a parameter (called degree of the condition), starting with $d = \min(n-f,\allowbreak f)$ and ending with d = 0, where $\mathcal{C}^{[0]}_f = \mathcal{C}_f$ . We prove that each one is strictly contained in the previous one: $\mathcal{C}^{[d]}_f\subset\mathcal{C}^{[d-1]}_f$ . Various properties of the hierarchy are also derived. It is shown that a class can be characterized in two equivalent but complementary ways: one is convenient for designing protocols while the other is for analyzing the class properties. The paper also defines a linear family of conditions that can be used to derive many specific conditions. In particular, for each d, two natural conditions are presented.The second part of the paper is devoted to the design of efficient condition-based protocols. A generic condition-based protocol is presented. This protocol can be instantiated with any condition C, $C\in\mathcal{C}^{[d]}_f$ , and requires at most $(2n + 1) \lceil{\log_2(\lceil{(f-d)/2\rceil} + 1)}$ shared memory read/write operations per process in the synchronization part of the protocol. Thus, the value ( f- d) represents the ? difficulty? of the class $\mathcal{C}^{[d]}_f$ . An improvement of the protocol for the conditions in $\mathcal{C}^{[0]}_f$ is also presented. [ABSTRACT FROM AUTHOR]

The Markowitz model of portfolio optimization quantifies the problem in a lucid form of only two criteria: the mean, representing the expected outcome, and the risk, a scalar measure of the variability of outcomes. The classical Markowitz model uses the variance as the risk measure, thus resulting in a quadratic optimization problem. Following Sharpe's work on linear approximation to the mean-variance model, many attempts have been made to linearize the portfolio optimization problem. There were introduced several alternative risk measures which are computationally attractive as (for discrete random variables) they result in solving linear programming (LP) problems. The LP solvability is very important for applications to real-life financial decisions where the constructed portfolios have to meet numerous side constraints and take into account transaction costs. The variety of LP solvable portfolio optimization models presented in the literature generates a need for their classification and comparison. It is the main goal of our work. The paper introduces a systematic overview of the LP solvable models with a wide discussion of their theoretical properties. This allows us to classify the models with respect to the types of risk or safety measures they use. The paper also provides the first complete computational comparison of the discussed models on real-life data. [ABSTRACT FROM AUTHOR]

The paper introduces a smoothing method to the lower order penalty function for constrained optimization problems. It is shown that, under some mild conditions, an optimal solution of the smoothed penalty problem is an approximate optimal solution of the original problem. Based on the smoothed penalty function, an algorithm is presented and its convergence is proved under some mild assumptions. Numerical examples show that the presented algorithm is effective. [ABSTRACT FROM AUTHOR]

This paper presents in-plane material filters for the Discrete Material Optimization method used for optimizing laminated composite structures. The filters make it possible for engineers to specify a minimum length scale which governs the minimum size of areas with constant material continuity. Consequently, engineers can target the available production methods, and thereby increase its manufacturability while the optimizer is free to determine which material to apply together with an optimum location, shape, and size of these areas with constant material continuity. By doing so, engineers no longer have to group elements together in so-called patches, so to statically impose a minimum length scale. The proposed method imposes the minimum length scale through a standard density filter known from topology optimization of isotropic materials. This minimum length scale is generally referred to as the filter radius. However, the results show that the density filter alone gives designs with large measures of non-discreteness. In order to obtain near discrete designs an additional threshold projection filter is applied, so to push the physical design variables towards their discrete bounds. However, because the projection filter is a non-linear function of the design variables, the projected variables have to be re-scaled in a final so-called normalization filter. This is done to prevent the optimizer in creating superior, but non-physical pseudo-materials. The method is demonstrated on a series of minimum compliance examples together with a minimum mass example, and the results show that the method is indeed capable of imposing a minimum length scale onto the optimized layup. [ABSTRACT FROM AUTHOR]

The paper suggests a new-to the best of the author's knowledge-characterization of decisions, which are optimal in the multi-objective optimization problem with respect to a definite proper preference cone, a Euclidean cone with a prescribed angular radius. The main idea is to use the angle distances between the unit vector and points of utility space. A necessary and sufficient condition for the optimality in the form of an equation is derived. The first-order necessary optimality conditions are also obtained. [ABSTRACT FROM AUTHOR]

In the present paper, we consider the inequality constrained vector problem with continuously Fréchet differentiable objective functions and constraints. We obtain second-order necessary optimality conditions of Karush-Kuhn-Tucker type for weak efficiency. A new second-order constraint qualification of Zangwill type is introduced. It is applied in the optimality conditions. Some connections with other constraint qualifications are established. [ABSTRACT FROM AUTHOR]

This paper deals with a nonsmooth semi-infinite multiobjective/vector optimization problem (SIMOP, for short). We first establish necessary and sufficient conditions for (local) strongly isolated solutions and (local) positively properly efficient solutions of an SIMOP. Then, we propose a dual problem to the SIMOP under consideration and examine weak and strong duality relations between them. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage method of transit hub location optimization is proposed. Firstly, alternative hub locations are determined by evaluating the attractiveness of each alternative location. The attractiveness of alternative location can be obtained by three aspects: the accessibility, the aggregation of transit lines and the layout of transit lines. Based on the alternative hub location set, considering the benefit of passenger and operator, a multi-objective model is presented to design transit hub location in a city. Then, a multi-objective genetic algorithm is also developed to solve the model. Finally, the data of Dalian city is used to validate the two-stage method for transit hub location optimization. The results show that the chosen alternative locations are almost cover main land use of Dalian city by using the attractiveness-based model. This indicates that the attractiveness-based model is a feasible and efficient way to determine alternative hub locations. Furthermore, the solution of the transit hub location optimization accords with the actual conditions of Dalian city. This indicates that the proposed multi-objective model and algorithm can consider the quantity, location and cost of transit hubs. [ABSTRACT FROM AUTHOR]

In this paper, we extend the notion of person-by-person (pbp) optimization to binary decision spaces. The novelty of our approach is the adaptation to a dynamic team context of notions borrowed from the pseudo-boolean optimization field as completely local-global or unimodal functions and submodularity. We also generalize the concept of pbp optimization to the case where groups of m decisions makers make joint decisions sequentially, which we refer to as mbm optimization. The main contribution is a description of sufficient conditions, verifiable in polynomial time, under which a pbp or an mbm optimization algorithm converges to the team-optimum. As a second contribution, we present a local and greedy algorithm characterized by approximate decision strategies (i.e., strategies based on a local state vector) that return the same decisions as in the complete information framework (where strategies are based on full state vector). As a last contribution, we also show that there exists a subclass of submodular team problems, recognizable in polynomial time, for which the pbp optimization converges for at least an opportune initialization of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a generalized fractional programming problem with linear constraints and the objective function is the sum of the functions of the form Minimize … Subject to the linear constraints Ax ≥ b, and x ≥ 0 where a'x+p , c'x + q and e'x + r are linear functions has been discussed. Solution of the above generalized fractional programming problem can be recovered by reducing it to a fractional programming problem. [ABSTRACT FROM AUTHOR]

Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-hard, and for most practical problems there are no efficient algorithms that yield exact solutions. A popular heuristic replaces the rank function with the nuclear norm-equal to the sum of the singular values-of the decision variable and has been shown to provide the optimal low rank solution in a variety of scenarios. In this paper, we assess the practical performance of this heuristic for finding the minimum rank matrix subject to linear equality constraints. We characterize properties of the null space of the linear operator defining the constraint set that are necessary and sufficient for the heuristic to succeed. We then analyze linear constraints sampled uniformly at random, and obtain dimension-free bounds under which our null space properties hold almost surely as the matrix dimensions tend to infinity. Finally, we provide empirical evidence that these probabilistic bounds provide accurate predictions of the heuristic's performance in non-asymptotic scenarios. [ABSTRACT FROM AUTHOR]

A new type of public key cryptosystem, called MST3, has been recently introduced on the basis of covers and logarithmic signatures for non-abelian finite groups. The class of Suzuki 2-groups has been proposed for a possible realization of the generic scheme. Due to their simple structure, the groups enable us to study the security of the system and also provide an efficient implementation. An earlier relevant result of the cryptanalysis has shown that the transversal logarithmic signatures are unfit for use in this realization. In this paper we present a revised version of MST3 for the Suzuki 2-groups and show a thorough study of its security. Using heuristic and algebraic methods we establish strong lower bounds for the workload of conceivable direct attacks on the private key of the scheme. We then develop a powerful chosen plaintext attack which allows us to rule out the usage of a certain class of logarithmic signatures. In addition, we show a class of logarithmic signatures withstanding this attack and thus to our knowledge they could be used in the realization of the scheme. Finally, we describe and discuss the implementation issues of the scheme in detail and include data of its performance obtained from an experimental result. [ABSTRACT FROM AUTHOR]

Abstract: In oblique shape from shading (SfS), the illumination direction is essential for recovering the 3D surface of a shaded image. On the other hand, fast marching methods (FMM) are SfS algorithms that use the mechanism of wave propagation to reconstruct the surface. In this paper, the estimation of illumination direction is addressed and we model it as an optimization problem. The idea is to minimize the inconsistency of wave propagation of FMM during the reconstruction. As the consistency of wave propagation is a multi-modal function of illumination direction, genetic algorithm (GA) is utilized. The proposed algorithm is examined on four synthetic models and a real world object. The experimental results show that the proposed algorithm is superior to benchmark methods. [Copyright &y& Elsevier]

Abstract: The paper presents a formal methodology for the hull form optimization in calm and rough water using wash waves and selected dynamic responses, respectively. Parametric hull form modeling is used to generate the variant hull forms with some of the form parameters modified, which are evaluated in the optimization scheme based on evolutionary strategies. Rankine-source panel method and strip theories are used for the hydrodynamic evaluation. The methodology is implemented in the optimization of a double-chine, planing hull form. Furthermore, a dual-stage optimization strategy is applied on a modern fast displacement ferry. The effect of the selected optimization parameters is presented and discussed. [Copyright &y& Elsevier]

Abstract: In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden''s necessary optimality conditions. [Copyright &y& Elsevier]

Abstract: The Basic Curvature Equations of Locally Tool Positioning (BCELTP) are an accurate description of the relationships between the second order approximations of the cutter surface, the tool envelope surface and the designed surface, which was proposed in our previous paper [Gong Hu, Cao Li-Xin, Liu Jian. Second order approximation of tool envelope surface for 5-axis machining with single point contact. Computer-Aided Design 2008;40:604–15]. Based on them, for a given tool path with single cutter contact point, a new local optimization method of tool positions is presented to maximize the machining strip width by minimizing the relative normal curvature between the tool envelope surface and the designed surface. Since the BCELTP are accurate analytical expressions, the proposed optimization method of tool positions is accurate and effective in computation. Furthermore, another new optimization method of tool positions based on a dual-parameter envelope is subsequently proposed. The most interesting point is that it will result in the same results as the method based on the BCELTP. It also proves the correctness of the method based on the BCELTP from a different angle. Finally, several examples are given to prove its effectiveness and accuracy. [Copyright &y& Elsevier]

The global exponential stability and the exponential convergence rate for time-delay systems with nonlinear uncertainties are investigated. A novel exponential stability criterion for the system is derived using the Lyapunov method. These stability conditions are formulated as linear matrix inequalities (LMIs), which can be easily solved by various convex optimization algorithms. The issue of exponential stability for time-delay systems with nonlinear uncertainties using generalized eigen value problem (GEVP) approach remains open, which motivates this paper. Numerical examples are given to illustrate the usefulness of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a modified degree-based strategy is proposed for pinning control of complex networks under constrained control capacity. Limited by the total control strength, the pinning strategy design problem is reconsidered as a constrained optimization problem. To provide a fast and effective solution, a decrease-and-conquer approach has been designed to assign the best control strengths to the pinned highest-degree nodes. Simulation results have demonstrated that the obtained strategy provides an outperformed control effect on various kinds of complex networks, as compared with strategies derived from degree, betweenness or closeness using evenly-distributed control strength. [ABSTRACT FROM AUTHOR]

In this paper, we consider the convex quadratic optimization problem with simultaneous perturbation in the right-hand side of the constraints and the linear term of the objective function with different parameters. The regions with invariant optimal partitions as well as the behaviour of the optimal value function on the regions are investigated. We show that identifying these regions can be done in polynomial time in the output size. An algorithm for identifying all invariancy regions is presented. Some implementation details as well as a numerical example are discussed. [ABSTRACT FROM AUTHOR]

Deep drawing is an extensively used press working process since it eliminates expensive machining and welding operations and enables the production of components at a very high rate. The workpiece material used in a deep drawing process is anisotropic in nature, due to a prior thermomechanical treatment. Earing is one of the major defects observed in a deep drawing process due to the anisotropic nature of the sheet material. Knowledge about the ear formation in deep drawing allows a prior modification of the process, which can result in a defect-free final product with financial savings. In this paper, a recently proposed anisotropic yield criteria by Barlat et al. for rolled sheets is used to model the anisotropy for simulating the earing defect in square and circular cup drawing processes. The effect of the tooling geometry and process parameters on the ear formation is studied. It is shown that, in the square cup, the uneven metal flow rate, rather than the material anisotropy, is mainly responsible for the flange earing. Finite element formulation, based on the updated Lagrangian approach, is employed for the analysis. The stresses are updated in a material frame and the logarithmic strain measure is used, which allows the use of a large increment size. Isotropic hardening is assumed, and it follows a power law. Inertia forces are neglected due to small accelerations. The modified Newton–Raphson iterative technique is used to solve the nonlinear incremental equations. [ABSTRACT FROM AUTHOR]

This paper presents a simplified algorithm for the profiling of the rack gear tool to generate the active profile associated with rolling centrodes. The algorithm is based on the enveloping surfaces principles and reduces the computational problem by proposing an approximate representation of tool’s profile using only three or four points on the profile to be generated by using Bezier representations of the surfaces. The algorithm computes the coefficients of a Bezier substitution polynomial instead of computing hundreds of points on the active profile. This simplifies the solution methodology and requires only a limited number of points along the profile to be generated. The algorithm is computationally efficient and can also be used in the case when the active profile to be generated is physically measured at a few points. Examples include mill tools, splined shafts, and gears. [ABSTRACT FROM AUTHOR]

Fuzzy linear programming (FLP ) is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming contains fuzzy constrains or crisp objectives function or contains crisp constrains with fuzzy objectives function, which called fuzzy linear programming (FLP) with triplet fuzzy numbers consist a hybrid fuzzy. The crisp constrains used in the problems of types (= or =) with a proposed optimization fuzzy objectives and fuzzy constrains. In this paper proposed method for solving fuzzy linear programming problem by using Two-phase technique to solve the problem and to determine the optima crisp objectives. [ABSTRACT FROM AUTHOR]

In this paper, an efficient computation method is developed for solving a general class of minmax optimal control problems, where the minimum deviation from the violation of the continuous state inequality constraints is maximized. The constraint transcription method is used to construct a smooth approximate function for each of the continuous state inequality constraints. We then obtain an approximate optimal control problem with the integral of the summation of these smooth approximate functions as its cost function. A necessary condition and a sufficient condition are derived showing the relationship between the original problem and the smooth approximate problem. We then construct a violation function from the solution of the smooth approximate optimal control problem and the original continuous state inequality constraints in such a way that the optimal control of the minmax problem is equivalent to the largest root of the violation function, and hence can be solved by the bisection search method. The control parametrization and a time scaling transform are applied to these optimal control problems. We then consider two practical problems: the obstacle avoidance optimal control problem and the abort landing of an aircraft in a windshear downburst. [ABSTRACT FROM AUTHOR]

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios. [ABSTRACT FROM AUTHOR]

Abstract: The paper is devoted to cold-formed thin-walled channel beams with open or closed profile of drop flanges. Geometric properties of two C-sections are described with consideration of warping functions and warping inertia moments. Optimization criterion and the dimensionless objective function as a quality measure are defined. Constraints of feasible solutions are strength, global and local buckling conditions and also geometric condition. Analytical solutions of the problems of global and local buckling for thin-walled beams are presented. Results of numerical investigation of optimization problem are compared and presented in tables and figures. [Copyright &y& Elsevier]

In this paper, some necessary and sufficient optimality conditions for the weakly efficient solutions of vector optimization problems (VOP) with finite equality and inequality constraints are shown by using two kinds of constraints qualifications in terms of the MP subdifferential due to Ye. A partial calmness and a penalized problem for the (VOP) are introduced and then the equivalence between the weakly efficient solution of the (VOP) and the local minimum solution of its penalized problem is proved under the assumption of partial calmness. [ABSTRACT FROM AUTHOR]

In this paper we present our experience with the optimization of atomic clusters under the binary Lennard-Jones potential. This is a generalization of the single atom type Lennard-Jones model to the case in which atoms of two different types (and 'sizes') interact within the same cluster. This problem has a combinatorial structure which increases complexity and requires strategies to be revised in order to take into account such new aspects. Our approach has been a very effective one: we have been able not only to confirm most putative optima listed in the Cambridge Cluster Database, but also to find 95 improved solutions. [ABSTRACT FROM AUTHOR]