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On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

Dynamic constrained optimization problems have received increasing attention in recent years. We study differential evolution which is one of the high performing class of algorithms for constrained continuous optimization in the context of dynamic constrained optimization. The focus of our investigations are repair methods which are crucial when dealing with dynamic constrained problems. Examining recently introduced benchmarks for dynamic constrained continuous optimization, we analyze different repair methods with respect to the obtained offline error and the success rate in dependence of the severity of the dynamic change. Our analysis points out the benefits and drawbacks of the different repair methods and gives guidance to its applicability in dependence on the dynamic changes of the objective function and constraints.

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Notes

  1. 1.

    This global optima is an approximation, which is the best solution found by DE in 50 runs for the current time.

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Acknowledgement

This work has been supported through Australian Research Council (ARC) grants DP140103400 and DP160102401.

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Authors and Affiliations

  1. Optimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia

    Maria-Yaneli Ameca-Alducin, Maryam Hasani-Shoreh & Frank Neumann

Authors
  1. Maria-Yaneli Ameca-Alducin
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  2. Maryam Hasani-Shoreh
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  3. Frank Neumann
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Corresponding author

Correspondence to Maria-Yaneli Ameca-Alducin .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Ameca-Alducin, MY., Hasani-Shoreh, M., Neumann, F. (2018). On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_55

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_55

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  • Online ISBN: 978-3-319-77538-8

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