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Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained 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 an updated 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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Acknowledgments

The first author acknowledges the financial support from CONCYTEG as part of the plan “Investigadores Jóvenes—DPP-2014” (Project 14-IJ-DPP-Q182-11). The second author is also affiliated to the UMI LAFMIA 3175 CNRS at CINVESTAV-IPN. He also acknowledges the financial support from CONACyT Project No. 221551.

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This is an updated version of the paper that appeared in 4OR, 11(3), 201–228 (2013a).

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Segura, C., Coello, C.A.C., Miranda, G. et al. Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization. Ann Oper Res 240, 217–250 (2016). https://doi.org/10.1007/s10479-015-2017-z

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