An elastic support-based optimization model is developed for a machinery mounting system comprising a vibrating machine supported on an elastic structure by multiple resilient mounts. This model is used to investigate...
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An elastic support-based optimization model is developed for a machinery mounting system comprising a vibrating machine supported on an elastic structure by multiple resilient mounts. This model is used to investigate the design optimization of an X-Y motion stage mounting system used in microelectronics wire-bonding equipment. By varying the stiffness coefficients of the resilient mounts while constraining the dynamic displacement amplitudes of the X-Y motion stage, the total force transmitted from the X-Y motion stage (the vibrating machine) to the equipment table (the elastic support structure) is minimized at each frequency interval in the frequency range of interest for different stiffnesses of the equipment table. The results show that, when the equipment table is relatively flexible, the total transmitted force minimized by the model developed is significantly lower than that minimized using a conventional rigid support-based optimization model at some critical frequency. When the equipment table is sufficiently rigid, both models provide almost the same predictions of the total transmitted force.
Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests;the incorporation needs to be simple enough that the solut...
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Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests;the incorporation needs to be simple enough that the solution method is tractable, yet complex enough to ensure the validity of the ultimate solution. We introduce a framework for constraint incorporation that identifies a minimal acceptable level of complexity and defines two basic types of constraint incorporation which (with combinations) cover nearly all popular numerical methods for constrained optimization, including trust region methods, penalty methods, barrier methods, penalty-multiplier methods, and sequential quadratic programming methods. The broad application of our framework relies on addition and chain rules for constraint incorporation which we develop here.
An efficient method based on the sequential quadratic programming (SQP) algorithm for the linear antenna arrays pattern synthesis with prescribed nulls in the interference direction and minimum side lobe levels by the...
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An efficient method based on the sequential quadratic programming (SQP) algorithm for the linear antenna arrays pattern synthesis with prescribed nulls in the interference direction and minimum side lobe levels by the complex weights of each array element is presented. In general, the pattern synthesis technique that generates a desired pattern is a greatly nonlinear optimization problem. SQP method is a versatile method to solve the general nonlinear constrained optimization problems and is much simpler to implement. It transforms the nonlinear minimization problem to a sequence of quadratic subproblem that is easier to solve, based on a quadratic approximation of the Lagrangian function. Several numerical results of Chebyshev pattern with the imposed single, multiple, and broad nulls sectors are provided and compared with published results to illustrate the performance of the proposed method. (C) 2007 Wiley Periodicals, Inc.
In this paper, we propose a BFGS (Broyden-Fletcher-Goldfarb-Shanno)-SQP (sequential quadratic programming) method for nonlinear inequality constrained optimization. At each step, the method generates a direction by so...
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In this paper, we propose a BFGS (Broyden-Fletcher-Goldfarb-Shanno)-SQP (sequential quadratic programming) method for nonlinear inequality constrained optimization. At each step, the method generates a direction by solving a quadraticprogramming subproblem. A good feature of this subproblem is that it is always consistent. Moreover, we propose a practical update formula for the quasi-Newton matrix. Under mild conditions, we prove the global and superlinear convergence of the method. We also present some numerical results.
The super-resolution time delay estimation in multipath environment is very important for many applications. Conventional super-resolution approaches can only deal with signals with wideband and flat spectra. In this ...
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The super-resolution time delay estimation in multipath environment is very important for many applications. Conventional super-resolution approaches can only deal with signals with wideband and flat spectra. In this paper, we propose a novel super-resolution time delay estimation method that can treat signals with narrowband spectra. In our method, the time delay estimation is first transformed into the frequency domain, in which the problem is converted into the parameter estimation of sinusoidal signals with lowpass envelopes. Then a MUSIC-type algorithm taking account of the envelope variation is applied to achieve the super-resolution estimation. Time delay estimation in active and passive systems are considered. Simulation results confirm that the proposed estimators provide better performance than the classical correlation approach and the conventional MUSIC algorithm for separating closely spaced signals with narrowband spectra.
In this paper, a modified SQP method is presented. The algorithm starts from an arbitrary initial point and can overcome the Maratos effect. Moreover, it avoid choosing the penalty parameters. Under some reasonable co...
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In this paper, a modified SQP method is presented. The algorithm starts from an arbitrary initial point and can overcome the Maratos effect. Moreover, it avoid choosing the penalty parameters. Under some reasonable conditions, the global convergence is shown. (c) 2006 Elsevier Inc. All rights reserved.
We propose a modified sequential quadratic programming method for solving mixed-integer nonlinear programming problems. Under the assumption that integer variables have a smooth influence on the model functions, i.e.,...
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We propose a modified sequential quadratic programming method for solving mixed-integer nonlinear programming problems. Under the assumption that integer variables have a smooth influence on the model functions, i.e., that function values do not change drastically when in-or decrementing an integer value, successive quadratic approximations are applied. The algorithm is stabilized by a trust region method with Yuan's second order corrections. It is not assumed that the mixed-integer program is relaxable or, in other words, function values are evaluated only at integer points. The Hessian of the Lagrangian function is approximated by a quasi-Newton update formula subject to the continuous and integer variables. Numerical results are presented for a set of 80 mixed-integer test problems taken from the literature. The surprising result is that the number of function evaluations, the most important performance criterion in practice, is less than the number of function calls needed for solving the corresponding relaxed problem without integer variables.
The shape optimization of rotating beams is carried out in order to minimize the vibrations. The objective is the maximization of the fundamental frequency with constraints on the beam mass and static tip deflections....
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The shape optimization of rotating beams is carried out in order to minimize the vibrations. The objective is the maximization of the fundamental frequency with constraints on the beam mass and static tip deflections. The finite-element method (FEM) is used to model the rotating beam and sequential quadratic programming (SQP) is used for the optimization. The effects of beam frequency maximization and hub-beam frequency maximization on the optimized shapes and the dynamic characteristics of these shapes are studied. The beam shapes are optimized for different speeds. The natural frequencies and time responses of these optimized shapes are studied using numerical simulation. Based on the numerical study, suggestions for formulating an appropriate optimization problem in a given context are made.
In this paper, a methodology is proposed for the multi-objective optimization of a multipass turning process. A real-parameter genetic algorithm (RGA) is used for minimizing the production time, which provides a nearl...
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In this paper, a methodology is proposed for the multi-objective optimization of a multipass turning process. A real-parameter genetic algorithm (RGA) is used for minimizing the production time, which provides a nearly optimum solution. This solution is taken as the initial guess for a sequential quadratic programming (SQP) code, which further improves the solution. Thereafter, the Pareto-optimal solutions are generated without using the cost data. For any Pareto-optimal solution, the cost of production can be calculated at a higher level for known cost data. An objective method based on the linear programming model is proposed for choosing the best among the Pareto-optimal solutions. The entire methodology is demonstrated with the help of an example. The optimization is carried out with equal depths of cut for roughing passes. A simple numerical method has been suggested for estimating the expected improvement in the optimum solution if an unequal depth of cut strategy would have been employed.
This paper describes a methodology aimed at a rapid assessment of the minimum thrust requirement for a given aircraft configuration. This latter involves a tight coupling between flight mission analyses, engine perfor...
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This paper describes a methodology aimed at a rapid assessment of the minimum thrust requirement for a given aircraft configuration. This latter involves a tight coupling between flight mission analyses, engine performance, and optimization techniques. The flight performance analysis is used to assess the relatively significant constraints, thus rapidly identifying the feasible design space to meet specific requirements, considering a tradeoff between operation constraints and objectives often of conflicting nature. With conventional procedures, it is rather difficult to find the optimum design point (match point), because of many involved objectives and constraints. However, by adopting the actual methodology using a subroutine called KSOPT, which solves constraints optimization problem using Kreisselmeier-Steinhauser envelope function formulation, associated with a deterministic optimization algorithm, either sequential quadratic programming or modified method of feasible direction, the overall procedure becomes simpler, because there is only one objective and a constraint, thus avoiding separate optimizations. This present methodology is therefore more effective from the point of view of rapidity in searching for the optimum match point, and may provide guidance in identifying a potential propulsion system and the necessary data serving to develop a derivative (growth) engine.
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