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A local approximation based multi-objective optimization algorithm with applications.

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Titel: A local approximation based multi-objective optimization algorithm with applications.
Autoren: Gobbi, Massimiliano, Guarneri, Paolo, Scala, Leonardo, Scotti, Lorenzo
Quelle: Optimization & Engineering; Sep2014, Vol. 15 Issue 3, p619-641, 23p
Abstract: Optimization techniques are useful tools to the design of complex systems. Especially in case of multiple conflicting performance indexes, the knowledge of the tradeoffs by means of Pareto optimality can help the designer to achieve the best solution. Due to the increasing power of the computing tools, more and more accurate and time consuming models are used. In this case, the Pareto set computation can be a hard task (the Pareto set can be nonconvex, nonlinearities and discontinuities can occur) and the efficiency and the accuracy become crucial features for an optimization algorithm. In this paper an optimization algorithm based on local approximation of the objective and constraints functions is presented and tested with some well known test functions. The optimal design of the suspension system of a ground vehicle is performed by the new algorithm in order to reach the best tradeoff by means of road holding, comfort, working space and cornering behavior. The numerical results show that the proposed algorithm has good accuracy and high efficiency if compared to some widely used methods. The results are explained providing some general observations on the efficiency of local approximation based algorithm an other well known algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Optimization & Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A local approximation based multi-objective optimization algorithm with applications.
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  Data: <searchLink fieldCode="AR" term="%22Gobbi%2C+Massimiliano%22">Gobbi, Massimiliano</searchLink><br /><searchLink fieldCode="AR" term="%22Guarneri%2C+Paolo%22">Guarneri, Paolo</searchLink><br /><searchLink fieldCode="AR" term="%22Scala%2C+Leonardo%22">Scala, Leonardo</searchLink><br /><searchLink fieldCode="AR" term="%22Scotti%2C+Lorenzo%22">Scotti, Lorenzo</searchLink>
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  Data: Optimization & Engineering; Sep2014, Vol. 15 Issue 3, p619-641, 23p
– Name: Abstract
  Label: Abstract
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  Data: Optimization techniques are useful tools to the design of complex systems. Especially in case of multiple conflicting performance indexes, the knowledge of the tradeoffs by means of Pareto optimality can help the designer to achieve the best solution. Due to the increasing power of the computing tools, more and more accurate and time consuming models are used. In this case, the Pareto set computation can be a hard task (the Pareto set can be nonconvex, nonlinearities and discontinuities can occur) and the efficiency and the accuracy become crucial features for an optimization algorithm. In this paper an optimization algorithm based on local approximation of the objective and constraints functions is presented and tested with some well known test functions. The optimal design of the suspension system of a ground vehicle is performed by the new algorithm in order to reach the best tradeoff by means of road holding, comfort, working space and cornering behavior. The numerical results show that the proposed algorithm has good accuracy and high efficiency if compared to some widely used methods. The results are explained providing some general observations on the efficiency of local approximation based algorithm an other well known algorithms. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Optimization & Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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