Simultaneous optimization of photostrictive actuator locations, numbers and light intensities for structural shape control using hierarchical genetic algorithm

Shape variation induced by mismachining tolerance, humidity and temperature of the working environment, material wear and aging, and unknown external load disturbances have a relatively large influence on the dynamic shape of a mechanical structure. When integrating piezoelectric elements into the main mechanical structure, active control of the structural shape is realized by utilizing the inverse piezoelectric effect. This paper presents a mathematical model regarding piezoelectric intelligent structure shape control. We also applied a genetic algorithm, and given a piezoelectric intelligent cantilever plate with both ends affected by a certain load, optimal shape control results of piezoelectric materials were analyzed from different perspectives (precision reference or cost reference). The mathematical model and results indicate that, by optimizing a certain number of piezoelectric actuators, high-precision active shape control can be realized.

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FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

10.26434/chemrxiv.7808351.v1 ◽

2019 ◽

Author(s):

Carmen Guguta ◽

Jan M.M. Smits ◽

Rene de Gelder

Keyword(s):

Genetic Algorithm ◽

Powder Diffraction ◽

Diffraction Data ◽

Correlation Functions ◽

Cross Correlation ◽

Simultaneous Optimization ◽

Powder Diffraction Data ◽

Structure Solution ◽

Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

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Simultaneous optimization of thermal and electrical conductivity of high density polyethylene-carbon particle composites by artificial neural networks and multi-objective genetic algorithm

Computational Materials Science ◽

10.1016/j.commatsci.2021.110956 ◽

2022 ◽

Vol 201 ◽

pp. 110956

Author(s):

Miguel García-Carrillo ◽

Adriana B. Espinoza-Martínez ◽

Luis F. Ramos-de Valle ◽

Saúl Sánchez-Valdés

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Electrical Conductivity ◽

Artificial Neural Networks ◽

High Density Polyethylene ◽

Carbon Particle ◽

High Density ◽

Simultaneous Optimization ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

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Hierarchical Genetic Algorithm for B-Spline Surface Approximation of Smooth Explicit Data

Mathematical Problems in Engineering ◽

10.1155/2014/706247 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

C. H. Garcia-Capulin ◽

F. J. Cuevas ◽

G. Trejo-Caballero ◽

H. Rostro-Gonzalez

Keyword(s):

Genetic Algorithm ◽

Geometric Modeling ◽

Data Set ◽

Surface Approximation ◽

B Spline ◽

B Splines ◽

Spline Surface ◽

Spline Surfaces ◽

Surface Dimension ◽

Hierarchical Genetic Algorithm

B-spline surface approximation has been widely used in many applications such as CAD, medical imaging, reverse engineering, and geometric modeling. Given a data set of measures, the surface approximation aims to find a surface that optimally fits the data set. One of the main problems associated with surface approximation by B-splines is the adequate selection of the number and location of the knots, as well as the solution of the system of equations generated by tensor product spline surfaces. In this work, we use a hierarchical genetic algorithm (HGA) to tackle the B-spline surface approximation of smooth explicit data. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots for each surface dimension and the B-spline coefficients simultaneously. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth surfaces and comparison with a successful method have been included.

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HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN

International Journal of Neural Systems ◽

10.1142/s0129065702001023 ◽

2002 ◽

Vol 12 (01) ◽

pp. 31-43 ◽

Cited By ~ 14

Author(s):

GARY YEN ◽

HAIMING LU

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Cost Function ◽

Design Procedure ◽

Optimal Solution ◽

Traditional Learning ◽

Feed Forward Neural Network ◽

Optimal Solution Set ◽

Hierarchical Genetic Algorithm

In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi-layer Perceptron networks and radial-basis function networks. Based upon the chosen cost function, a linear weight combination decision-making approach has been applied to derive an approximated Pareto-optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two-objective optimization problem.

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