Multi-island Genetic Algorithm Opetimization of Suspension System

Zhang, Mengjie; Xu, Liwei; Gao, Ruizhen

doi:10.11591/telkomnika.v10i7.1563

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…A certain number of individuals between the islands migrate after a certain number of generations. Thus, Multi-Island GA can prevent the problem of “premature” by maintaining the diversity of the population (20) . In addition, the calculation speed of Multi-Island GA can be greater than that of traditional genetic algorithms.…”

Section: Methodsmentioning

confidence: 99%

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Cruise missile head shape optimisation using an adaptive sampling surrogate model

et al. 2018

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High-precision response of the surrogate model is desired in the process of optimisation. An excessive number of sampling points will increase the cost of the calculation. The appropriate number of sampling points cannot only guarantee the accuracy of the surrogate model but also save the calculation cost. The purpose of this research is to demonstrate the eventuality of using an adaptive surrogate model for optimisation problems. The adaptive surrogate model is built on an adaptive sampling approach and an extended radial basis function (ERBF). The adaptive sampling is an approach that new sampling points are placed in the blank area and all the sampling points are uniformly distributed in the design region using Multi-Island GA. The precision of the ERBF surrogate model is checked using standard error measure to determine whether the surrogate model should be updated or not. This adaptive surrogate model is used to optimise a cruise missile head shape. Aerodynamic and stealthy performance of the cruise missile head shape are considered in this research. Different global objective function and different weight factor are used to research the aerodynamic and stealthy performance in this optimisation process. The results show that the drag is reduced with a slender head shape and the radar-cross section (RCS) value is reduced with a short head shape.

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Section: Methodsmentioning

confidence: 99%

“…The vector α is defined by solving the Equation (20). The Euclidean distances and nonradial basis values that sampling point x relative to all data points (x 1 , x 2 , …, x np ) into…”

Section: Table 1 the Description Of Non-radial Basis Functionsmentioning

confidence: 99%

Cruise missile head shape optimisation using an adaptive sampling surrogate model

et al. 2018

View full text Add to dashboard Cite

show abstract

“…A certain number of individuals between the islands migrate after a certain number of generations. Thus, Multi-Island GA can prevent the problem of "premature" by maintaining the diversity of the population [15]. In addition, the calculation speed of Multi-Island GA can be greater than that of traditional genetic algorithms.…”

Section: Optimization Methodsmentioning

confidence: 99%

Structure modal optimization of a strapdown inertial navigation system for an electric helicopter using an adaptive surrogate model

Guo¹,

Zheng²,

Ang³

2017

J. vibroeng.

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The purpose of this research is to prove the eventuality of using a novel adaptive surrogate model for optimization problems. The adaptive surrogate model is based on iteration sampling and extended radial basis function (ERBF). This method improves the precision by a means that new sample points is placed in the blank area and all the sample points is uniformly distributed in the design region. The precision of the surrogate model is checked using standard error measure to determine whether updating the surrogate model or not. Since the prediction of modal frequencies require structure modal simulations. In order to decrease the number of computer simulations, a Multi-Island GA approach is combined with the adaptive surrogate model to find the optimum modal frequencies of a strapdown inertial navigation system for electric helicopters. The strapdown inertial navigation system is comprised of damping material, counterweight material and inertial navigation sensor. This is a multi-objective functions optimization problem since the modal frequencies are considered from mode 1 to mode 6 in this paper. Several weights of multi-objective functions are utilized to research the modal frequencies. The whole number of 15 sampling points is picked to build the primary surrogate model using Latin hypercube sampling (LHS). The results of adaptive surrogate model show that two new sampling points are needed to reform the precision of the surrogate model under the condition of 2 % confidence bounds. The structure modal optimization results show that the choice of the weights for the multi-objective functions has a major effect on the final optimum modal frequencies. Time-and frequency-domain analysis indicated that the optimum modal frequencies are far away from the excitation frequencies to avoid strapdown inertial navigation system resonance as far as possible.

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“…A certain number of individuals between the islands migrate after a certain number of generations. Thus, MIGA can prevent the problem of "premature" by maintaining the diversity of the population [29]. Table 4 presents the main parameters of MIGA.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems

Wang

et al. 2018

Applied Sciences

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This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs.

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Multi-island Genetic Algorithm Opetimization of Suspension System

Cited by 12 publications

References 8 publications

Cruise missile head shape optimisation using an adaptive sampling surrogate model

Cruise missile head shape optimisation using an adaptive sampling surrogate model

Structure modal optimization of a strapdown inertial navigation system for an electric helicopter using an adaptive surrogate model

Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems

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