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Toward a New Family of Hybrid Evolutionary Algorithms

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Evolutionary Multi-Criterion Optimization (EMO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11411))

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Abstract

Multi-objective optimization problems (MOPs) arise in a natural way in diverse knowledge areas. Multi-objective evolutionary algorithms (MOEAs) have been applied successfully to solve this type of optimization problems over the last two decades. However, until now MOEAs need quite a few resources in order to obtain acceptable Pareto set/front approximations. Even more, in certain cases when the search space is highly constrained, MOEAs may have troubles when approximating the solution set. When dealing with constrained MOPs (CMOPs), MOEAs usually apply penalization methods. One possibility to overcome these situations is the hybridization of MOEAs with local search operators. If the local search operator is based on classical mathematical programming, gradient information is used, leading to a relatively high computational cost. In this work, we give an overview of our recently proposed constraint handling methods and their corresponding hybrid algorithms. These methods have specific mechanisms that deal with the constraints in a wiser way without increasing their cost. Both methods do not explicitly compute the gradients but extract this information in the best manner out of the current population of the MOEAs. We conjecture that these techniques will allow for the fast and reliable treatment of CMOPs in the near future. Numerical results indicate that these ideas already yield competitive results in many cases.

The authors acknowledge support for CONACyT project No. 285599 and IPN project SIP20181450.

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Notes

  1. 1.

    Time series of domestic price indexes were obtained from the National Institute of Statistics and Geography of Mexico (INEGI, by its Spanish acronym).

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Authors and Affiliations

  1. ESFM, Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional Edif, 9, Unidad Profesional Adolfo López Mateos, Zacatenco, Mexico

    Lourdes Uribe, Oliver Schütze & Adriana Lara

  2. Department of Applied Mathematics and Systems, UAM Cuajimalpa. Dr. Rodolfo Quintero Chair, Mexico City, Mexico

    Lourdes Uribe, Oliver Schütze & Adriana Lara

Authors
  1. Lourdes Uribe
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  2. Oliver Schütze
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  3. Adriana Lara
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Corresponding author

Correspondence to Lourdes Uribe .

Editor information

Editors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Kalyanmoy Deb

  2. Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  4. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  5. University of Jyvaskyla, Jyväskylä, Finland

    Kaisa Miettinen

  6. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sanaz Mostaghim

  7. Cornell University, Ithaca, NY, USA

    Patrick Reed

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Uribe, L., Schütze, O., Lara, A. (2019). Toward a New Family of Hybrid Evolutionary Algorithms. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-12598-1

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