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A multi-objective DIRECT algorithm for ship hull optimization

Author

Listed:
  • E. F. Campana

    (Consiglio Nazionale delle Ricerche)

  • M. Diez

    (Consiglio Nazionale delle Ricerche)

  • G. Liuzzi

    (Consiglio Nazionale delle Ricerche)

  • S. Lucidi

    (Sapienza Università di Roma)

  • R. Pellegrini

    (Consiglio Nazionale delle Ricerche)

  • V. Piccialli

    (Università degli Studi di Roma “Tor Vergata”)

  • F. Rinaldi

    (Università di Padova)

  • A. Serani

    (Consiglio Nazionale delle Ricerche)

Abstract

The paper is concerned with black-box nonlinear constrained multi-objective optimization problems. Our interest is the definition of a multi-objective deterministic partition-based algorithm. The main target of the proposed algorithm is the solution of a real ship hull optimization problem. To this purpose and in pursuit of an efficient method, we develop an hybrid algorithm by coupling a multi-objective DIRECT-type algorithm with an efficient derivative-free local algorithm. The results obtained on a set of “hard” nonlinear constrained multi-objective test problems show viability of the proposed approach. Results on a hull-form optimization of a high-speed catamaran (sailing in head waves in the North Pacific Ocean) are also presented. In order to consider a real ocean environment, stochastic sea state and speed are taken into account. The problem is formulated as a multi-objective optimization aimed at (i) the reduction of the expected value of the mean total resistance in irregular head waves, at variable speed and (ii) the increase of the ship operability, with respect to a set of motion-related constraints. We show that the hybrid method performs well also on this industrial problem.

Suggested Citation

  • E. F. Campana & M. Diez & G. Liuzzi & S. Lucidi & R. Pellegrini & V. Piccialli & F. Rinaldi & A. Serani, 2018. "A multi-objective DIRECT algorithm for ship hull optimization," Computational Optimization and Applications, Springer, vol. 71(1), pages 53-72, September.
    • Handle: RePEc:spr:coopap:v:71:y:2018:i:1:d:10.1007_s10589-017-9955-0
    DOI: 10.1007/s10589-017-9955-0
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    References listed on IDEAS

    as
    1. Giampaolo Liuzzi & Stefano Lucidi & Veronica Piccialli, 2010. "A partition-based global optimization algorithm," Journal of Global Optimization, Springer, vol. 48(1), pages 113-128, September.
    2. G. Liuzzi & S. Lucidi & V. Piccialli, 2016. "Exploiting derivative-free local searches in DIRECT-type algorithms for global optimization," Computational Optimization and Applications, Springer, vol. 65(2), pages 449-475, November.
    3. G. Di Pillo & G. Liuzzi & S. Lucidi & V. Piccialli & F. Rinaldi, 2016. "A DIRECT-type approach for derivative-free constrained global optimization," Computational Optimization and Applications, Springer, vol. 65(2), pages 361-397, November.
    4. Remigijus Paulavičius & Yaroslav Sergeyev & Dmitri Kvasov & Julius Žilinskas, 2014. "Globally-biased Disimpl algorithm for expensive global optimization," Journal of Global Optimization, Springer, vol. 59(2), pages 545-567, July.
    5. Qunfeng Liu & Jinping Zeng, 2015. "Global optimization by multilevel partition," Journal of Global Optimization, Springer, vol. 61(1), pages 47-69, January.
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    Cited by:

    1. Riccardo Pellegrini & Andrea Serani & Giampaolo Liuzzi & Francesco Rinaldi & Stefano Lucidi & Matteo Diez, 2020. "Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
    2. G. Cocchi & M. Lapucci, 2020. "An augmented Lagrangian algorithm for multi-objective optimization," Computational Optimization and Applications, Springer, vol. 77(1), pages 29-56, September.
    3. Alberto Lovison & Kaisa Miettinen, 2021. "On the Extension of the DIRECT Algorithm to Multiple Objectives," Journal of Global Optimization, Springer, vol. 79(2), pages 387-412, February.
    4. Filipe Alves & Lino A. Costa & Ana Maria A. C. Rocha & Ana I. Pereira & Paulo Leitão, 2022. "The Sustainable Home Health Care Process Based on Multi-Criteria Decision-Support," Mathematics, MDPI, vol. 11(1), pages 1-19, December.
    5. Gabriele Eichfelder & Kathrin Klamroth & Julia Niebling, 2021. "Nonconvex constrained optimization by a filtering branch and bound," Journal of Global Optimization, Springer, vol. 80(1), pages 31-61, May.
    6. Matteo Lapucci & Pierluigi Mansueto, 2023. "A limited memory Quasi-Newton approach for multi-objective optimization," Computational Optimization and Applications, Springer, vol. 85(1), pages 33-73, May.

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