Genetic algorithm for design optimization of multibody dynamics using differential–algebraic equation integrators

Based on the general model of design optimization of multibody dynamics, a modified genetic algorithm with adaptive crossover and mutation rates is developed to find optimal design variables which satisfy the dynamic constraints and obtain optimum objective values. Generalized-α projection method an...

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Veröffentlicht in:Advances in mechanical engineering Jg. 7; H. 4; S. 1
1. Verfasser: Ding, Jieyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.04.2015
Sage Publications Ltd
SAGE Publishing
Schlagworte:
generalized-α
projection method
modified genetic algorithm
variational integrators
Design optimization
differential–algebraic equations
ISSN:1687-8132, 1687-8140
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Abstract Based on the general model of design optimization of multibody dynamics, a modified genetic algorithm with adaptive crossover and mutation rates is developed to find optimal design variables which satisfy the dynamic constraints and obtain optimum objective values. Generalized-α projection method and higher order variational integrators with Lagrangian polynomial and Gauss quadrature formula are used to solve differential–algebraic equations during optimization process. Efficiency and accuracy of the numerical results obtained by intelligent design optimization with different differential–algebraic equation solving methods are compared.
AbstractList Based on the general model of design optimization of multibody dynamics, a modified genetic algorithm with adaptive crossover and mutation rates is developed to find optimal design variables which satisfy the dynamic constraints and obtain optimum objective values. Generalized-α projection method and higher order variational integrators with Lagrangian polynomial and Gauss quadrature formula are used to solve differential–algebraic equations during optimization process. Efficiency and accuracy of the numerical results obtained by intelligent design optimization with different differential–algebraic equation solving methods are compared.
Author Ding, Jieyu
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10.1007/s00158-007-0176-5
10.1063/2.1206309
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10.1007/978-3-642-25453-6_35
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Issue 4
Keywords generalized-α
projection method
modified genetic algorithm
variational integrators
Design optimization
differential–algebraic equations
Language English
License This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (http://www.uk.sagepub.com/aboutus/openaccess.htm).
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Haddadpour, Firouz-Abadi, Fotouhi 2008; 36
Ding, Pan 2014; 2014
Ding, Pan, Chen 2009; 30
Liu, Wang 1999; 15
Ding, Pan 2012; 2
Etman, van Campen, Schoofs 1998; 2
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