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Evolutionary Dynamic Optimization of a Continuously Variable Transmission for Mechanical Efficiency Maximization

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MICAI 2005: Advances in Artificial Intelligence (MICAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3789))

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Abstract

This paper presents a dynamic optimization approach based on the differential evolution (DE) strategy which is applied to the concurrent optimal design of a continuously variable transmission (CVT). The structure-control integration approach is used to state the concurrent optimal design as a dynamic optimization problem which is solved using the Constraint Handling Differential Evolution (CHDE) algorithm. The DE strategy is compared with the sequential approach. The results presented here demonstrate that the DE strategy is less expensive than the sequential approach from the computational implementation point of view.

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, CINVESTAV-IPN, Apdo. Postal 14-740, 07300, Mexico DF, Mexico

    Jaime Alvarez-Gallegos & Carlos Alberto Cruz Villar

  2. Engineering and Technology Department, Universidad Autónoma de Tlaxcala, Calz. Apizaquito S/N Km. 15, 90300, Apizaco, Tlax., Mexico

    Edgar Alfredo Portilla Flores

Authors
  1. Jaime Alvarez-Gallegos
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  2. Carlos Alberto Cruz Villar
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  3. Edgar Alfredo Portilla Flores
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Editor information

Editors and Affiliations

  1. National Polytechnic Institute, Center for Computing Research, 07738, Mexico City, México

    Alexander Gelbukh

  2. Technológico de Monterrey (ITESM), Campus Ciudad de México (CCM), Calle del Puente 222, Col. Ejudos de Huipulco, 14360 DF, Tlalpan, Mexico

    Álvaro de Albornoz

  3. Center for Intelligent Systems, Tecnológico de Monterrey, Campus Monterrey, 64849, Monterrey, N.L., Mexico

    Hugo Terashima-Marín

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© 2005 Springer-Verlag Berlin Heidelberg

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Alvarez-Gallegos, J., Villar, C.A.C., Flores, E.A.P. (2005). Evolutionary Dynamic Optimization of a Continuously Variable Transmission for Mechanical Efficiency Maximization. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_111

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  • DOI: https://doi.org/10.1007/11579427_111

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29896-0

  • Online ISBN: 978-3-540-31653-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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