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Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming

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Learning and Intelligent Optimization (LION 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14286))

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Abstract

Many efficient and effective approaches have been proposed in the evolutionary computation literature for solving constrained optimization problems. Most of the approaches assume that both the objective function and the constraints are black-box functions, while a few of them can take advantage of the gradient information. On the other hand, when the gradient information is available, the most versatile approaches are arguably the ones coming from the numerical optimization literature. Perhaps the most popular methods in this field are sequential quadratic programming and interior point. Despite their success, those methods require accurate gradients and usually require a well-shaped initialization to work as expected. In the paper at hand, a novel hybrid method, named UPSO-QP, is presented that is based on particle swarm optimization and borrows ideas from the numerical optimization literature and sequential quadratic programming approaches. The proposed method is evaluated on numerous constrained optimization tasks from simple low dimensional problems to high dimensional realistic trajectory optimization scenarios, and showcase that is able to outperform other evolutionary algorithms both in terms of convergence speed as well as performance, while also being robust to noisy gradients and bad initialization.

This work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “3rd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Acronym: NOSALRO, Project Number: 7541).

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Notes

  1. 1.

    The code is available at https://github.com/NOSALRO/algevo.

  2. 2.

    We use the implementation provided by scipy.

  3. 3.

    We use the implementation provided by the Ipopt library.

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Authors and Affiliations

  1. Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, 26110, Patras, Greece

    Konstantinos I. Chatzilygeroudis & Michael N. Vrahatis

Authors
  1. Konstantinos I. Chatzilygeroudis
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  2. Michael N. Vrahatis
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Correspondence to Konstantinos I. Chatzilygeroudis .

Editor information

Editors and Affiliations

  1. InsideOpt, Dover, DE, USA

    Meinolf Sellmann

  2. Bielefeld University, Bielefeld, Germany

    Kevin Tierney

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Chatzilygeroudis, K.I., Vrahatis, M.N. (2023). Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming. In: Sellmann, M., Tierney, K. (eds) Learning and Intelligent Optimization. LION 2023. Lecture Notes in Computer Science, vol 14286. Springer, Cham. https://doi.org/10.1007/978-3-031-44505-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-44505-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44504-0

  • Online ISBN: 978-3-031-44505-7

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