Corrigendum: Design and optimization of a low voltage RF switch MEMS capacitance using genetic algorithm and Taguchi method

At present, there is a trend to design a hard disk with a larger capacity, a faster revolution speed, and a precise mechanism. Therefore, the influence of data transmission with respect to vibration is obvious. To reduce interference of the vibrational impact on the data transmission efficiency, vibrational abatement on the hard disk using the damping material becomes crucial. Because of the complicated relationship for the damping coefficient and spring constant to the hardness (D1, D2, and D3) of the damping material installed under the hard disk, it is difficult to theoretically assess an optimal hardness combination of the damping material. Therefore, an alternative way by using an experimental study in conjunction with Taguchi method, an Artificial Neural Network (ANN), and a GA Method is proposed. In this paper, a hard disk is placed on a vibration tester that is an analogue to a dynamic vibrational circumstance induced by a vibrational base. The data transfer rate of the hard disk will be detected by using IOmeter software under various base-excitation accelerations, tilted angles of the hard disk, and targeted frequencies. To reduce the vibrational impact on the data transmission efficiency, an assessment of an optimal three-layer damping material installed under the hard disk using the Taguchi method, the Artificial Neural Network (ANN), and the GA Method is proposed. Before the optimization of the damping material is performed, the required experimental sets (the hardness for three layers of damping material) of the data transmission testing with respect to various design parameters will be determined by using the Taguchi method. The ANN, a simplified objective function (OBJ), will be established by inputting the hardness of three layers of damping material and their related data transmission efficiency at three targeted frequencies. Thereafter, the optimal hardness for three layers of the damping material will be obtained using a genetic algorithm (GA). Consequently, the optimal hardness of the three-layer damping material with respect to various tilted angles and target frequencies will be assessed.

PurposeThe purpose of this paper is to investigate prediction and optimization of multiple performance characteristics in the wire electrical discharge machining (wire-EDM) process of SKD 61 (AISI H13) tool steel.Design/methodology/approachThe experimental studies were conducted under varying wire-EDM process parameters, which were arc on time, on time, open voltage, off time and servo voltage. The optimized responses were recast layer thickness (RLT), surface roughness (SR) and surface crack density (SCD). Arc on time was set at two different levels, whereas the other four parameters were set at three different levels. Based on Taguchi method, an L18 mixed-orthogonal array was selected for the experiments. Further, three methods, namely grey relational analysis (GRA), backpropagation neural network (BPNN) and genetic algorithm (GA), were applied separately. GRA was performed to obtain a rough estimation of optimum drilling parameters. The influences of drilling parameters on multiple performance characteristics were determined by using percentage contributions. BPNN architecture was determined to predict the multiple performance characteristics. GA method was then applied to determine the optimum wire-EDM parameters.FindingsThe minimum RLT, SR and SCD could be obtained by setting arc on time, on time, open voltage, off time and servo voltage at 2 ms, 3 ms, 90 volt, 10 ms and 38 volt, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the responses.Originality/valueThere were no publications regarding multi-response optimization using a combination of GRA and BPNN-based GA methods during wire-EDM process available.

Optimization of solar still equipped with TEC by Taguchi and genetic algorithm methods: A case study for sustainable drinking water supply in the villages of Sistan and Baluchestan with new technologies

Purpose This paper aims to design, optimize and simulate the Radio Frequency (RF) micro electromechanical system (MEMS) Switch which is stimulated by electrostatically voltage. Design/methodology/approach The geometric structure of the switch was extracted based on the design of Taguchi-based experiment using the mathematical programming and obtaining objective function by the genetic meta-heuristic algorithm. Findings The RF parameters of the switch were calculated for the design of Taguchi-based S11 = −5.649 dB and S21 = −46.428 dB at the working frequency of 40 GHz. The pull-in voltage of the switch was 2.8 V and the axial residual stress of the proposed design was obtained 28 MPa and the design of Taguchi-based S11 = −4.422 dB and S21 = −48.705dB at the working frequency of 40 GHz. The pull-in voltage of the switch was 2.5 V and the axial residual stress of the proposed design was obtained 25 MPa. Originality/value A novel complex strategy in the design and optimization of capacitive RF switch MEMS modeling is proposed.

In this paper, it is first proposed that genetic algorithm and Taguchi method can be employed in the optimal design of DC-DC converter with LC snubber. The purpose of this optimal design is to lower the spike voltage V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dsp</sub> of power switch and hence reduce the cost in manufacturing circuit. It is great educational value for students and engineers. For the first step, we investigate the circuit parameters which will affect Vd and subsequently converge the range of circuit parameter value by means of genetic algorithm, and conduct the optimal design of prototype converter with Taguchi method. Compared with spike voltage V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dsp</sub> of non-optimal design circuit, the effect of optimal design is revealed. In suppressing spike voltage, the V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dsp</sub> measured from optimal design circuit is 115 V, and is actually 28.1% reduced. Therefore, using genetic algorithm and Taguchi method in the optimal design of converter with LC snubber is a more economic, practical and efficient of circuit design, which meanwhile can be easily applied to other electronic circuits and accomplish the optimal design of various quality characteristics.

In this paper, it is first proposed that genetic algorithm and Taguchi method can be employed in the optimal design of DC-DC converter with RCD snubber. The purpose of this optimal design is to lower the spike voltage V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dsp</sub> of power switch and hence reduce the cost in manufacturing circuit. It is of great educational value for students and engineers. For the first step, we investigate the circuit parameters which will affect V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ds</sub> and subsequently converge the range of circuit parameter by means of genetic algorithm and conduct the optimal design of prototype circuit with Taguchi method. Compared with spike voltage of non-optimal design circuit V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dsp</sub> , the effect of optimal design is revealed. In suppressing voltage spike, the V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dsp</sub> measured from optimal design circuit is 210 V, and is actually 8.2% reduced. Therefore, using genetic algorithm and Taguchi method in the optimal design of converter with RCD snubber is a more economic, practical and efficient of circuit design, which meanwhile can be easily applied to other electronic circuits and accomplish the optimal design of various quality characteristics.

Seismic velocity estimation from well log data with genetic algorithms in comparison to neural networks and multilinear approaches

Numerical analysis of circular straight mufflers equipped with three chambers at high-order-modes

Parameter identification is the process of constructing the mathematical model of a dynamic system based on measured dynamic responses. A non-classical search method namely genetic algorithm (GA) is employed in this study, which has several advantages over classical system identification techniques. Nevertheless, direct application of GA does not necessarily work well, particularly with regards to computational efficiency in fine-tuning when the solution approaches the optimal value. Thus, a local search (LS) method is introduced into the GA approach to improve this capability. Numerical example of a cantilever beam is presented to show the efficiency of the combined GA and LS method (GA-LS). The FE model of the cantilever beam is established with 10 beam elements. In this example, only the element properties associated with flapping motion are identified. The GA-LS method gives much better results than the GA method does. The mean error is reduced to 5.3% from 14.9% while to 14.5% from 47.2% for the maximum individual error. It is concluded that the GA-LS method is a more efficient search method than the GA method. The GA-LS method is then applied to identify the mass and stiffness parameters of a model helicopter blade using measured excitations and accelerations in this paper. Since the mass and stiffness of the model helicopter blade are not available, the only way to verify the identified parameters is to compare the ffequencies and mode shapes calculated from the identified parameters and the measured frequencies and modal shapes of the actual blade. The mean eiror of the frequencies is 0.9% and the maximum individual error 2.4%. The sequence of identified mode shapes is the same with measured mode shapes. Transactions on Engineering Sciences vol 43, © 2003 WIT Press, www.witpress.com, ISSN 1743-3533

Utilization of the geostationary satellite orbit (GSO) has reached full capacity in orbital arcs at certain longitudes. A variety of proposals and regulations have already been established to increase the number of possible satellite positions. The optimum satellite arrangement carried out by considering the geographical distribution of service areas is an effective strategy for efficient utilization of the GSO. The genetic algorithm (GA) method is applied here to optimize the satellite arrangement. Initially, an example of the optimum arrangement of eight satellites is demonstrated by using the GA method to ensure accuracy of the newly developed program. Subsequently, a multiple beam satellite system concept is introduced to further improve the efficient utilization of the GSO. Finally, 80 satellites (40 service areas) in the ITU Region 1 example is solved using the GA method, resulting in a quasi-optimum satellite arrangement solution. By using the GA method, the quasi-optimum solution can be obtained by very short (e.g., several ten seconds) calculation times.

In this paper, genetic algorithm (GA) method is applied to both positive and negative Sub Resolution Assist Features (SRAF) insertion rules. Simulation results and wafer data demonstrated that the optimized SRAF rules helped resolve the SRAF printing issues while dramatically improving the process window of the working layer. To find out the best practice to place the SRAF, model-based SRAF (MBSRAF), rule-based SRAF (RBSRAF) with pixelated OPC simulation and RBSRAF with GA method are thoroughly compared. The result shows the apparent advantage of RBSRAF with GA method.

Construction projects often go through delays due to various reasons, which create a dreadful financial influence on the project. For minimizing this scenario, cost and time optimization of a construction project is effectively used. Cost and time optimization method is the most effective and time efficient method with highest achievable performance under specific condition in a construction project. This method is mainly required for cost and time optimization in a construction project. This thesis work also highlights the various innovative techniques that are required for cost and time optimization of the project. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are considered the advanced innovative techniques which are being used continuously by the construction companies for cost and time optimization. The advance work of Genetic Algorithm(GA) method in the form of GA with Dev-C ++ 4.9.9.2, GA with Line of Balance(LOB), GA with Modified Adaptive Weight Approach (MAWA), GA with Critical Path Method (CPM) along with new methods Linear Programming (LP), Non-Linear Integer Programming Model (NLIP), Discounted Cash Flow Method (DCF), Maximum Flow-Minimal Cut Theory and Artificial Neural Networks Method (ANN) are also included in the various innovative techniques of cost and time optimization process. Furthermore, the method of Genetic Algorithm (GA) which is specified in the thesis work is classified into two parameters where the global parallel GA method provides more effectiveness and efficiency than coarse-grained parallel GA method. Also, it is found through researchers and investigators that the Non-Linear Integer Programming (NLIP) method and Line of Balance (LOB) with GA method both have an efficient and optimum solution for time cost trade-off problem, along with PSO method which is best for Pareto-compromise solution and Direct Cash Flow (DCF) method which optimizes cost and time within the project boundaries. Finally, it is observed GA along with its advanced parameters, ANN method and NLP techniques are better for solving time cost trade-off problems.

A novel tool for designing well placements by combination of modified genetic algorithm and artificial neural network

This report describes a GA (Genetic Algorithms) method that evolves multi-layered feedforward neural network architectures for specific mappings. The network is represented as a genotype that has six kinds of genes. They are a learning rate, a slant of sigmoid function, a coefficient of momentum term, an initializing weights range, the number of layers and the unit numbers of each layer. Genetic operators affect populations of these genotypes to produce adaptive networks with higher fitness values. We define three kinds of fitness functions that evaluate networks generated by the GA method. Their fitnesses are assessed for the generated network trained with BP (Back Propagation) algorithm by several network performances. In our experiments, we train the networks for the XOR mapping. They are designed systematically and easily using the GA method. These generated networks require fewer training cycles then networks used until now, and a rate of convergence is improved.

Although genetic algorithm and multi-objective optimization techniques are widely used to solve problems in the design and manufacturing area, further improvements are required to develop more efficient techniques regarding multi-objective optimization problems. The main goal of the present research is to further develop and strengthen the genetic algorithm based multi-objective optimization approach to generate real-world design solutions in the automotive industry. In this research, a new hybrid approach based on Taguchi's method and a genetic algorithm is presented to achieve better Pareto-optimal set solutions for multi-objective design optimization problems. In addition, fatigue damage and life are also considered to evaluate the results of the design optimization process. The validity and efficiency of the proposed approach are evaluated and illustrated with test problems taken from the literature. It is then applied to a vehicle component taken from the automotive industry.