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Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach

Author

Listed:
  • Alejandro Alvarado-Iniesta

    (Universidad Autónoma de Ciudad Juárez)

  • Luis Gonzalo Guillen-Anaya

    (Universidad Autónoma de Ciudad Juárez)

  • Luis Alberto Rodríguez-Picón

    (Universidad Autónoma de Ciudad Juárez)

  • Raul Ñeco-Caberta

    (Universidad Autónoma de Ciudad Juárez)

Abstract

This work addresses the optimization of an engine mount design from a multi-objective scenario. Our methodology is divided into three phases: phase one focuses on data collection through computer simulations. The objectives considered during the analyses are: total mass, first natural frequency and maximum von Mises stress. In phase two, a surrogate model by means of genetic programming is generated for each one of the objectives. Moreover, a local search procedure is incorporated into the overall genetic programming algorithm for improving its performance. Finally, in phase three, instead of steering the search to finding the approximate Pareto front, a local exploration approach based on a change in the weight space is used to lead a search into user defined directions turning the decision making more intuitive.

Suggested Citation

  • Alejandro Alvarado-Iniesta & Luis Gonzalo Guillen-Anaya & Luis Alberto Rodríguez-Picón & Raul Ñeco-Caberta, 2020. "Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 19-32, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1432-9
    DOI: 10.1007/s10845-018-1432-9
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    References listed on IDEAS

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    1. Hamed Bakhtiari & Mahdi Karimi & Sina Rezazadeh, 2016. "Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 463-473, April.
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    3. Qi Zhou & Longchao Cao & Hui Zhou & Xiang Huang, 2018. "Prediction of angular distortion in the fiber laser keyhole welding process based on a variable-fidelity approximation modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 719-736, March.
    4. Qinqin Fan & Xuefeng Yan, 2018. "Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 35-49, January.
    5. Oliver Schütze & Adanay Martín & Adriana Lara & Sergio Alvarado & Eduardo Salinas & Carlos Coello, 2016. "The directed search method for multi-objective memetic algorithms," Computational Optimization and Applications, Springer, vol. 63(2), pages 305-332, March.
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    Cited by:

    1. Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.

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