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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2013: Evolutionary Multi-Criterion Optimization pp 337–351Cite as

‘‘Whatever Works Best for You’’- A New Method for a Priori and Progressive Multi-objective Optimisation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7811))

Abstract

Various multi-objective evolutionary algorithms (MOEAs) have been developed to help a decision maker (DM) search for his/her preferred solutions to multi-objective problems. However, none of these approaches has catered simultaneously for the two fundamental ways that DM can specify his/her preferences: weights and aspiration levels. In this paper, we propose an approach named iPICEA-g that allows the DM to specify his preference in either format. iPICEA-g is based on the preference-inspired co-evolutionary algorithm (PICEA-g). Solutions are guided toward regions of interest (ROIs) to the DM by co-evolving sets of goal vectors exclusively generated in the ROIs. Moreover, a friendly decision making technique is developed for interaction with the optimization process: the DM specifies his preferences easily by interactively brushing his preferred regions in the objective space. No direct elicitation of numbers is required, reducing the cognitive burden on DM. The performance of iPICEA-g is tested on a set of benchmark problems and is shown to be good.

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Author information

Authors and Affiliations

  1. Department of Automatic Control & Systems Engineering, University of Sheffield, UK

    Rui Wang, Robin C. Purshouse & Peter J. Fleming

  2. Department of Systems Engineering, College of Information Systems and Management, National University of Defence Technology, ChangSha, China, 410073

    Rui Wang

Authors
  1. Rui Wang
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  2. Robin C. Purshouse
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  3. Peter J. Fleming
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Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Robin C. Purshouse

  2. Department of Automatic Control Systems and Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Peter J. Fleming

  3. Department of Informatics Engineering, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Carlos M. Fonseca

  4. Department of Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

  5. Unilever Research & Development Port Sunlight, Quarry Road East, CH63 3JW, Merseyside, UK

    Jane Shaw

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© 2013 Springer-Verlag Berlin Heidelberg

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Wang, R., Purshouse, R.C., Fleming, P.J. (2013). ‘‘Whatever Works Best for You’’- A New Method for a Priori and Progressive Multi-objective Optimisation. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_27

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  • DOI: https://doi.org/10.1007/978-3-642-37140-0_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37139-4

  • Online ISBN: 978-3-642-37140-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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