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Using multi-objective evolutionary algorithms for single-objective optimization

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Abstract

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers a survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.

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Notes

  1. In evolutionary algorithms, it is necessary to define a measure of performance for each individual that allows to compare it with respect to others. This way, the best solutions (with respect to this measure of performance) have a higher probability of being selected. This measure of performance is called fitness function and it is normally defined in terms of the objective function(s) that we aim to optimize (usually, a normalized version of the objective function(s) value(s) is adopted).

  2. The word “rand” indicates that individuals selected to compute the mutation values are chosen at random, “1” is the number of pairs of solutions chosen and finally “bin” means that a binomial recombination is used.

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Acknowledgments

The second author gratefully acknowledges support from CONACyT Project No. 103570. This work was also partially supported by the ec (FEDER) and the Spanish Ministry of Science and Innovation as part of the ‘Plan Nacional de i+d+i’, with contract number tin2011-25448.

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  1. Dpto. de Computación, UMI LAFMIA 3175 CNRS at CINVESTAV-IPN, Mexico, D.F., Mexico

    Carlos Segura

  2. Dpto. de Computación (Evolutionary Computation Group), CINVESTAV-IPN, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  3. Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, Tenerife, Spain

    Gara Miranda & Coromoto León

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  1. Carlos Segura
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  4. Coromoto León
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Correspondence to Carlos A. Coello Coello.

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Segura, C., Coello Coello, C.A., Miranda, G. et al. Using multi-objective evolutionary algorithms for single-objective optimization. 4OR-Q J Oper Res 11, 201–228 (2013). https://doi.org/10.1007/s10288-013-0248-x

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