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A Many-Objective Optimisation Decision-Making Process Applied to Automotive Diesel Engine Calibration

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Simulated Evolution and Learning (SEAL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6457))

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Abstract

A novel process has been developed for reducing complexity in real-world, high-dimensional, multi-objective optimisation problems. This approach relies on being able to identify and exploit local harmony between objectives to reduce dimensionality. To achieve this, a systematic and modular process has been designed to cluster the Pareto-optimal front and apply a rule-based Principal Component Analysis including preference articulation for potential objective reduction. This many-objective optimisation decision-making process is demonstrated on a real-world, automotive diesel engine calibration optimisation problem comprising six objectives. The complexity reduction process resulted in three- and four-objective sub-problems. In the former, a significant improvement was achieved in one of the retained objectives at very little cost to the others.

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Author information

Authors and Affiliations

  1. Powertrain Calibration & Development, Ford Motor Company, 15/2A-F13-C, Dunton, Technical Centre, Laindon, Basildon, Essex, SS15 6EE, U.K.

    Robert J. Lygoe & Mark Cary

  2. Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, U.K.

    Peter J. Fleming

Authors
  1. Robert J. Lygoe
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  2. Mark Cary
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  3. Peter J. Fleming
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Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  2. Department of Computer Science andl Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Arnab Bhattacharya

  3. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, 712302, Kharagpur, West Bengal, India

    Nirupam Chakraborti

  4. Department of Civil Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Partha Chakroborty  & Ashu Jain  & 

  5. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  6. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Joydeep Dutta

  7. Department of Chemical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Santosh K. Gupta

  8. Aspiring Minds, 24 Pusa Road, 110005, New Delhi, India

    Varun Aggarwal

  9. Warwick Business School, University of Warwick, CV4 7AL, Coventry, UK

    Jürgen Branke

  10. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    Sushil J. Louis

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

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Lygoe, R.J., Cary, M., Fleming, P.J. (2010). A Many-Objective Optimisation Decision-Making Process Applied to Automotive Diesel Engine Calibration. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_72

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  • DOI: https://doi.org/10.1007/978-3-642-17298-4_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17297-7

  • Online ISBN: 978-3-642-17298-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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