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Based on both white response and connotation expression are geometric progression in the most primitive grey differential equation of GM(1,1)x(0)(k)+ax(1)(k) = b, this paper begins with generation of the time response function's grey derivative at discrete points. Through derivative's definition, establishing a new GM(1,1) by optimizing grey derivative and background value. Then, getting the best coefficient c by introducing criterion function and it has proved that the new expression has the whitened exponent law coincident property and the whitened coefficient coincident property in theory. Finally, some examples show the new model has higher prediction precision. [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]

Due to the complexity of optimum replacement problems over finite time horizons various asymptotic criteria based upon fixed age replacement policies have been employed in the literature and in practice. In this paper the relationships between the optimum policies under three alternative cost criteria are considered. An ordering of the accounting costs under two of these is obtained, and for distributions with Increasing Hazard Rate an ordering of the optimum replacement time is derived. For a finite time horizon the policies are compared to the optimal sequential and fixed-age replacement polices through the example of a gamma distribution previously investigated by Barlow and Proschan (1962, 1965). [ABSTRACT FROM AUTHOR]

Abstract Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of swarm intelligence for solving continuous optimization problems as well as real world optimization problems. The Grey Wolf Optimizer is the only algorithm in the category of swam intelligence which is based on leadership hierarchy. This paper has three important aspects- Firstly, for improving the search ability by grey wolf a modified algorithm RW-GWO based on random walk has been proposed. Secondly, its performance is exhibited in comparison with GWO and state of art algorithms GSA, CS, BBO and SOS on IEEE CEC 2014 benchmark problems. A non-parametric test Wilcoxon and Performance Index Analysis has been performed to observe the impact of improving the leaders in the proposed algorithm. The results presented in this paper demonstrate that the proposed algorithm provide a better leadership to search a prey by grey wolves. The third aspect of the paper is to use the proposed algorithm and GWO on real life application problems. It is concluded from this article that RW-GWO algorithm is an efficient and reliable algorithm for solving not only continuous optimization problems but also for real life optimization problems. [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 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]

The single-sink, fixed-charge transportation problem has a variety of applications, including supplier selection, product distribution/fleet selection, and process selection. In this paper we present a dynamic programming algorithm for solving this important problem that is very easy to implement and that improves considerably in terms of computational attractiveness on the best methods in the literature. [ABSTRACT FROM AUTHOR]

This paper discusses the advantages of using spreadsheets for problem specification and report generation in optimization projects. It summarizes some of the mathematical programming software which is compatible with popular spreadsheets. A small production planning problem is used to illustrate the steps in input and processing of the results. Two programs are compared in detail. [ABSTRACT FROM AUTHOR]

This paper presents novel results on perfect reconstruction feedback quantizers (PRFQs), i.e., noise-shaping, predictive and sigma-delta A/D converters whose signal transfer function is unity. Our analysis of this class of converters is based upon an additive white noise model of quantization errors. Our key result is a formula that relates the minimum achievable MSE of such converters to the signal-to-noise ratio (SNR) of the scalar quantizer embedded in the feedback loop. This result allows us to obtain ana- lytical expressions that characterize the corresponding optimal filters. We also show that, for a fixed SNR of the scalar quantizer, the end-to-end MSE of an optimal PRFQ which uses the optimal filters (which for this case turn out to be HR) decreases exponentially with increasing oversampling ratio. Key departures from earlier work include the fact that fed back quantization noise is explicitly taken into account and that the order of the converter filters is not a priori restricted. [ABSTRACT FROM AUTHOR]

The goal of this paper is to give a rigorous justification for the Hessian-constrained problems introduced in [G. Bouchitté and I. Fragal`a, Arch. Ration. Mech. Anal., 184 (2007), pp. 257-284] and to show how they are linked to the optimal design of thin plates. To that aim, we study the asymptotic behavior of a sequence of optimal elastic compliance problems in the double limit when both the maximal height of the design region and the total volume of the material tend to zero. In the vanishing volume limit, a sequence of linear constrained first order vector problems is obtained, which in turn-in the vanishing thickness limit-produces a new linear constrained problem where both first and second order gradients appear. When the load is orthogonal to the plate, only the Hessian constraint is active, and we recover as a particular case the optimization problem studied in [G. Bouchitté and I. Fragal`a, Arch. Ration. Mech. Anal., 184 (2007), pp. 257-284] (see also [T. Lewinski and J. J. Telega, Arch. Mech. (Arch. Mech. Stos.), 53 (2001), pp. 457-485]). [ABSTRACT FROM AUTHOR]

Typically, the printing of contact patterns uses a dark-field (DF) mask in combination with a positive tone resist and positive tone development (PTD) process. PTD, which has a mature process and simulation model, had been widely applied in high-volume manufacturing. For the low aerial image quality of a DF mask in advanced node, PTD is substituted by negative tone development (NTD), which uses a positive tone resist and bright-field mask. Due to the high cost and immature simulation model of NTD process, it is worthwhile to extend PTD to some critical patterns. With the purpose of improving the resist profile and process window (PW) of the contact pattern with a PTD process in advanced node, an optimization method combined with the idea of a genetic algorithm is put forward. For performance of the optimized resist under the conditions of best focus and best dose, an evaluation based on the through pitch square contact patterns with the critical dimension (CD) fixed at 50 nm has been provided. The generalization performance of the optimized resist is also analyzed by a systematic method, which contains the resist profile and PW simulation on the base of through CD and through pitch contact patterns. The above simulation results verify the effectiveness and validity of the proposed optimization method. [ABSTRACT FROM AUTHOR]

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, we address the challenging task of finding the best alignment between two 3D objects by solving a global optimization problem in the space of rotations SO(3). The objective function to be optimized is a newly developed rotation-variant similarity measure, which is obtained directly from the object''s geometry and is entirely implemented on the GPU. By exploiting the modern GPU''s parallel architecture, we can process considerably greater amounts of data than a CPU implementation can do in the same amount of time. This allows us to create a similarity measure which combines speed and accuracy. The actual problem of rotation alignment is then solved by finding the global maximum of this similarity function in the space of rotations. A special rotation representation allows for an efficient local optimization on the manifold SO(3). Furthermore, unwanted local maxima can be avoided by a heuristic global optimization procedure which exploits rotational symmetry. Due to this common sense heuristics, the global search can be gradually reduced to a lower-dimensional problem up to a 1D line search to handle objects with high rotational symmetry. We show that our method is superior to existing normalization techniques such as PCA and provides a high degree of precision despite remarkably short runtimes. [Copyright &y& Elsevier]

Abstract: Analyzing the state of the art in a given field in order to tackle a new problem is always a mandatory task. Literature provides surveys based on summaries of previous studies, which are often based on theoretical descriptions of the methods. An engineer, however, requires some evidence from experimental evaluations in order to make the appropriate decision when selecting a technique for a problem. This is what we have done in this paper: experimentally analyzed a set of representative state-of-the-art techniques in the problem we are dealing with, namely, the road passenger transportation problem. This is an optimization problem in which drivers should be assigned to transport services, fulfilling some constraints and minimizing some function cost. The experimental results have provided us with good knowledge of the properties of several methods, such as modeling expressiveness, anytime behavior, computational time, memory requirements, parameters, and free downloadable tools. Based on our experience, we are able to choose a technique to solve our problem. We hope that this analysis is also helpful for other engineers facing a similar problem. [Copyright &y& Elsevier]

Abstract: This paper aims to show the use of the response surface methodology (RSM) in size optimization of an autonomous PV/wind integrated hybrid energy system with battery storage. RSM is a collection of statistical and mathematical methods which relies on optimization of response surface with design parameters. In this study, the response surface, output performance measure, is the hybrid system cost, and the design parameters are the PV size, wind turbine rotor swept area and the battery capacity. The case study is realized in ARENA 10.0, a commercial simulation software, for satisfaction of electricity consumption of the global system for mobile communications (GSM) base station at Izmir Institute of Technology Campus Area, Urla, Turkey. As a result, the optimum PV area, wind turbine rotor swept area, and battery capacity are obtained to be 3.95m2, 29.4m2, 31.92kWh, respectively. These results led to $37,033.9 hybrid energy system cost, including auxiliary energy cost. The optimum result obtained by RSM is confirmed using loss of load probability (LLP) and autonomy analysis. [Copyright &y& Elsevier]

Abstract: Applications for on-line data reconciliation and optimization must be efficient and numerically robust. The models in these applications are rarely changed and the same optimization problem is solved thousands of times with only minor changes in the parameters. This paper describes a suitable modeling framework for this type of applications that, with the aim of simplifying the creation of new models, makes the application robust and avoids numerical difficulties. The model is based on a unit model structure where first-order derivatives, scaling and initial values are properties of the unit model. A new scaling procedure is proposed based on equation and variable pairing. The modeling framework and the use of the proposed scaling procedure are demonstrated in two case studies, case 1 is simulation of a simple pipe model, case 2 is simulation, data reconciliation and optimization of a flash process. [Copyright &y& Elsevier]

Abstract: Torczon and several persons have designed pattern search methods for unconstrained optimization problem, bound constrained, and linearly constrained optimization problems. In this paper, we shall consider linear equality constrained problems as unconstrained ones using generalized pattern search algorithm, except for a feasible starting point. [Copyright &y& Elsevier]

This paper deals with the optimal production/maintenance (PM) policy for a deteriorating production system which may shift from the in-control state to the out-of-control state while producing items. The process is assumed to have a general shift distribution. Under the commonly used maintenance policy, equal-interval maintenance, the joint optimizations of the PM policy are derived such that the expected total cost per unit time is minimized. Different conditions for optimality, lower and upper bounds and uniqueness properties on the optimal PM policy are provided. The implications of another commonly used policy, to perform a maintenance action only at the end of the production run, are also discussed. Structural properties for the optimal policy are established so that an efficient solution procedure is obtained. In the exponential case, some extensions of the results obtained previously in the literature are presented. A numerical example is provided to illustrate the solution procedure for the optimal production and maintenance policy. [ABSTRACT FROM AUTHOR]

Studies of the convergence of algorithms often revolve around the existence of a function with respect to which monotonic descent is required. In this paper, we show that under relatively lenient conditions, "stage-dependent descent" (not necessarily monotonic) is sufficient to guarantee convergence. This development also provides the impetus to examine optimization algorithms. We show that one of the important avenues in the study of convergence, namely, the theory of epi-convergence imposes stronger conditions than are necessary to establish the convergence of an optimization algorithm. Working from a relaxation of epi-convergence, we introduce the notion of ∂-compatibility, and prove several results that permit relaxations of conditions imposed by previous approaches to algorithmic convergence. Finally, to illustrate the usefulness of the concepts, we combine stage-dependent descent with results derivable from ∂-compatibility to provide a basis for the convergence of a general algorithmic statement that might be used for stochastic and nondifferentiable optimization. [ABSTRACT FROM AUTHOR]

One of the central trends in the optimization community over the past several years has been the steady improvement of general-purpose solvers. A logical next step in this evolution is to combine mixed-integer linear programming, constraint programming, and global optimization in a single system. Recent research in the area of integrated problem solving suggests that the right combination of different technologies can simplify modeling and speed up computation substantially. Nevertheless, integration often requires special-purpose coding, which is time consuming and error prone. We present a general-purpose solver, SIMPL, that allows its user to replicate (and sometimes improve on) the results of custom implementations with concise models written in a high-level language. We apply SIMPL to production planning, product configuration, machine scheduling, and truss structure design problems on which customized integrated methods have shown significant computational advantage. We obtain results that either match or surpass the original codes at a fraction of the implementation effort. [ABSTRACT FROM AUTHOR]

The article presents a study on an optimal sensor placement for underwater threat detection. It states that the optimization framework for optimal sensor placement for underwater threat detection has been established. This considered a single-period and multiperiod detection models. Moreover, it is mentioned that numerical experiments have shown that the algorithm consistently outperform current optimization technique for optimal sensor placement.

Minimization of such functions is considered, where some arguments are related to the final function by intermediate functions with discontinuity points, but other arguments have only 0 and 1 for the allowed values, although the theoretical generalization allows also intermediate values. Both of the circumstances create difficulties in the use of the gradient method. We solve the first problem by approximation, primarily by a square polynomial obtained using the integral form of the least squares method, and later by the partial sums of orthogonal series of the wave function treated with the logarithmic averages method. The second problem can be solved with the help of the planes, which have been taken in the n-dimensional space in such a way that any allowed point on the side of the space relative to this plane is better than all the points on the other side. [ABSTRACT FROM AUTHOR]

This research, a new thrust-allocation algorithm based on penalty programming is developed to minimize the fuel consumption of offshore vessels/platforms with dynamic positioning system. The role of thrust allocation is to produce thruster commands satisfying required forces and moments for position-keeping, while fulfilling mechanical constraints of the control system. The developed thrust-allocation algorithm is mathematically formulated as an optimization problem for the given objects and constraints of a dynamic positioning system. Penalty programming can solve the optimization problems that have nonlinear object functions and constraints. The developed penalty-programming thrust-allocation method is implemented in the fully-coupled vessel–riser–mooring time-domain simulation code with dynamic positioning control. Its position-keeping and fuel-saving performance is evaluated by comparing with other conventional methods, such as pseudo-inverse, quadratic-programming, and genetic-algorithm methods. In this regard, the fully-coupled time-domain simulation method is applied to a turret-moored dynamic positioning assisted FPSO (floating production storage offloading). The optimal performance of the penalty programming in minimizing fuel consumption in both 100-year and 1-year storm conditions is demonstrated compared to pseudo-inverse and quadratic-programming methods. [ABSTRACT FROM AUTHOR]