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International Conference on Machine Learning, Optimization, and Data Science

LOD 2018: Machine Learning, Optimization, and Data Science pp 191–203Cite as

Designing Ships Using Constrained Multi-objective Efficient Global Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

A modern ship design process is subject to a wide variety of constraints such as safety constraints, regulations, and physical constraints. Traditionally, ship designs are optimized in an iterative design process. However, this approach is very time consuming and is likely to get stuck in local optima. Not only does this optimization problem have complex constraints, it also consists of multiple objectives like resistance, stability and cost.

This constrained multi-objective optimization problem can be dealt with much more efficiently than through the traditional approach. In this paper, we propose a novel global optimization algorithm that explores the design space with the help of integrated software tools that are capable of simultaneous evaluation of the ship objectives and constraints. The optimization algorithm proposed uses the -Metric-Selection-based Efficient Global Optimization (SMS-EGO) in combination with constraint handling techniques from an algorithm called Self-Adjusting Constrained Optimization by Radial Basis Function Approximation (SACOBRA). Since the evaluation of these ship designs is expensive in terms of computational effort, it is crucial for the algorithm to find feasible near-optimal solutions in as few evaluations as possible.

In this paper, it is shown that the proposed Constrained Efficient Global Optimization (CEGO) algorithm can significantly improve ship designs by automatic optimization using a small evaluation budget.

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Notes

  1. 1.

    NAPA Oy, Release 2017.3-3 (2018), NAPA software, http://www.NAPA.fi/.

  2. 2.

    C-Job Naval Architects, Ship Design and Engineering (2018), https://c-job.com/.

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Author information

Authors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Roy de Winter, Bas van Stein & Thomas Bäck

  2. Research & Development, C-Job Naval Architects, Hoofddorp, The Netherlands

    Matthys Dijkman

Authors
  1. Roy de Winter
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  2. Bas van Stein
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  3. Matthys Dijkman
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  4. Thomas Bäck
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Corresponding author

Correspondence to Roy de Winter .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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de Winter, R., van Stein, B., Dijkman, M., Bäck, T. (2019). Designing Ships Using Constrained Multi-objective Efficient Global Optimization. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_16

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  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

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