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Bio-Inspired Algorithms and Preferences for Multi-objective Problems

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Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

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Abstract

Multi-objective optimization evolutionary algorithms have been applied to solve many real-life decision problems. Most of them require the management of trade-offs between multiple objectives. Reference point approaches highlight a preferred set of solutions in relevant areas of Pareto frontier and support the decision makers to take more confidence evaluation. This paper extends some well-known algorithms to work with collective preferences and interactive techniques. In order to analyse the results driven by the online reference points, two new performance indicators are introduced and tested against some synthetic problem.

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Notes

  1. 1.

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Acknowledgments

This work was partially funded by CNPq BJT Project 407851/2012-7, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015.

Author information

Authors and Affiliations

  1. Universidade Federal Fluminense, Rio de Janeiro, Brazil

    Daniel Cinalli, Luis Martí & Ana Cristina Bicharra Garcia

  2. Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil

    Nayat Sanchez-Pi

Authors
  1. Daniel Cinalli
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  2. Luis Martí
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  3. Nayat Sanchez-Pi
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  4. Ana Cristina Bicharra Garcia
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Corresponding author

Correspondence to Daniel Cinalli .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Cinalli, D., Martí, L., Sanchez-Pi, N., Garcia, A.C.B. (2016). Bio-Inspired Algorithms and Preferences for Multi-objective Problems. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_20

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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