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Ranking Methods in Many-Objective Evolutionary Algorithms

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Nature-Inspired Algorithms for Optimisation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 193))

Abstract

This chapter presents a comparative study of different ranking methods on many-objective problems. The aim of this work is to investigate the effectiveness of different approaches in order to determine any possible limitations and/or advantages of each of the ranking methods studied and, in general, their performance. Thus, the results may help practitioners to select a suitable ranking method for a problem at hand, and can serve researchers as a guideline to develop new ranking schemes or further extensions of the Pareto optimality relation.

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Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), Av. IPN No. 2508, Col. San Pedro Zacatenco, México, D.F. 07360, Mexico

    Antonio López Jaimes, Luis Vicente Santana Quintero & Carlos A. Coello Coello

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  1. Antonio López Jaimes
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  2. Luis Vicente Santana Quintero
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  3. Carlos A. Coello Coello
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Editors and Affiliations

  1. Swinburne University of Technology, Sarawak Campus, Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia

    Raymond Chiong

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Jaimes, A.L., Quintero, L.V.S., Coello, C.A.C. (2009). Ranking Methods in Many-Objective Evolutionary Algorithms. In: Chiong, R. (eds) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00267-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-00267-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00266-3

  • Online ISBN: 978-3-642-00267-0

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