Mixed optimization combinatorial method for constructing covering arrays

Covering arrays are used for generating tests for interfaces with a large number of parameters. In this paper, a new method is described for constructing homogeneous and heterogeneous covering arrays that is based on a combination of combinatorial and optimization methods. In a wide class of particular cases, the method speeds up the construction of arrays several times (depending on a particular case) compared with well-known, widely used optimization methods. In most cases, the sizes of the arrays obtained are approximately the same as those of the arrays constructed by other optimization methods; in a number of particular cases, one could obtain arrays that are smaller by 5–15%. The application range of the new method is analyzed.     

Low-noise gaas MESFET optimization using an orthogonal array approach

This article presents an experimental optimization of the noise figure of small-signal self-aligned FETs. An L₁₈ orthogonal array has been used to find the main effects of specific device parameters on noise performance at 18 GHz. This knowledge has been used to find an alternative device which shows a ～0.7-dB improvement in noise measure without requiring major process changes. Hybrid low-noise amplifiers built with the improved FETs confirm the noise performance and show record power performance for high dynamic range applications. © 1994 John Wiley & Sons, Inc.     

Design optimization of circular antenna arrays using Taguchi method

This paper presents the application of Taguchi method (TM) to design optimization of non-uniform circular antenna array (CAA) for suppression of sidelobe levels (SLLs). TM, a robust design approach, takes signal-to-noise ratio and orthogonal array tools from the statistical design of experiments. These tools allow instead of full factorial parametric analysis minimize the design parameters; thus, increase the convergence speed and generate more accurate solutions. TM is used to determine an optimal set of amplitudes and positions of CAA for 8, 10, and 12 elements. Comparison of the results of the TM with those of latest meta-heuristic algorithms in the literature reveals that the CAA design with TM provides the best SLL reduction performance in all cases.

     

Discrete variable optimization of plate structures using dual methods

This study presents an efficient method for optimum design of plate and shell structures, when the design variables are continuous or discrete. Both sizing and shape design variables are considered. First the structural responses, such as element forces, are approximated in terms of some intermediate variables. By substituting these approximate relations into the original design problem, an explicit nonlinear approximate design task with high quality approximation is achieved. This problem with continuous variables can be solved very efficiently by means of numerical optimization techniques, the results of which are then used for discrete variable optimization. Now, the approximate problem is converted into a sequence of second level approximation problems of separable form, each of which is solved by a dual strategy with discrete design variables. The approach is efficient in terms of the number of required structural analyses, as well as the overall computational cost of optimization. Examples are offered and compared with other methods to demonstrate the features of the proposed method.     

Mixed constrained image filter design using particle swarm optimization

This article describes an evolutionary image filter design for noise reduction using particle swarm optimization (PSO), where mixed constraints on the circuit complexity, power, and signal delay are optimized. First, the evaluated values of correctness, complexity, power, and signal delay are introduced to the fitness function. Then PSO autonomously synthesizes a filter. To verify the validity of our method, an image filter for noise reduction was synthesized. The performance of the resultant filter by PSO was similar to that of a genetic algorithm (GA), but the running time of PSO is 10% shorter than that of GA.     

Discrete variable optimization of frames using a strain energy criterion

This paper reports on recent advances concerning a new method for the weight (discrete variable) optimization of frames under both stress and multi-displacement constraints using a criterion based on the distribution of the virtual strain energy density. The proposed methodology is based on an idea that has been previously demonstrated and successfully applied on truss structures. The influence of each participating beam is defined through properly normalized sensitivity factors. An iterative formula, similar to that used for trusses, is proposed for updating the second moment of inertia followed by an improvement of shape variables (e.g. height, width, thickness) on the cross-section. The proposed method is successfully compared with the sequential quadratic programming method and other techniques published in the most recent literatures.     

Multiobjective optimization using variable complexity modelling for control system design

A multi-stage design approach that uses a multiobjective genetic algorithm as the framework for optimization and multiobjective preference articulation, and an H_infty loop-shaping technique are used to design controllers for a gas turbine engine. A non-linear model is used to assess performance of the controller. Because the computational load of applying multiobjective genetic algorithm to this control strategy is very high, a neural network and response surface models are used in order to speed up the design process within the framework of a multiobjective genetic algorithm. The final designs are checked using the original non-linear model.     

A new design optimization framework based on immune algorithm and Taguchi's method

This paper describes an innovative optimization approach that offers significant improvements in performance over existing methods to solve shape optimization problems. The new approach is based on two-stages which are (1) Taguchi's robust design approach to find appropriate interval levels of design parameters (2) Immune algorithm to generate optimal solutions using refined intervals from the previous stage. A benchmark test problem is first used to illustrate the effectiveness and efficiency of the approach. Finally, it is applied to the shape design optimization of a vehicle component to illustrate how the present approach can be applied for solving shape design optimization problems. The results show that the proposed approach not only can find optimal but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.     

To analyze binary data from mixed-level orthogonal arrays

Orthogonal arrays of mixed levels have been found useful in setting up highly fractional factorial experiments. When the responses from such experiments are binary, logistic regression analysis is used to analyze such data. Usually, the maximum likelihood estimates of parameters under the logistic regression model need to be found by an iterative procedure in order to test the importance of factors. In this article, a simple approximate procedure is proposed and compared to the maximum likelihood method with an example.