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Archivers for the representation of the set of approximate solutions for MOPs

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Abstract

In this paper we address the problem of computing suitable representations of the set of approximate solutions of a given multi-objective optimization problem via stochastic search algorithms. For this, we will propose different archiving strategies for the selection of the candidate solutions maintained by the generation process of the stochastic search process, and investigate them further on analytically and empirically. For all archivers we will provide upper bounds on the approximation quality as well as on the cardinality of the limit solution set. We conclude this work by a comparative study on some test problems in order to visualize the effect of all novel archiving strategies.

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Authors and Affiliations

  1. Computer Science Department, Cinvestav-IPN, 07360, Mexico City, Mexico

    O. Schütze & C. Hernández

  2. University of Lille 1, 59655, Villeneuve d’Ascq, France

    E-G. Talbi

  3. School of Engineering, University of California, Merced, CA, 95343, USA

    J. Q. Sun & Y. Naranjani

  4. Department of Mechanics, Tianjin University, Tianjin, China

    F.-R. Xiong

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  1. O. Schütze
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  2. C. Hernández
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  3. E-G. Talbi
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  4. J. Q. Sun
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  5. Y. Naranjani
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  6. F.-R. Xiong
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Correspondence to C. Hernández.

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Schütze, O., Hernández, C., Talbi, EG. et al. Archivers for the representation of the set of approximate solutions for MOPs. J Heuristics 25, 71–105 (2019). https://doi.org/10.1007/s10732-018-9383-z

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  • DOI: https://doi.org/10.1007/s10732-018-9383-z

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