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Surrogate-Assisted Particle Swarm with Local Search for Expensive Constrained Optimization

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Bioinspired Optimization Methods and Their Applications (BIOMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10835))

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Abstract

This paper develops a surrogate-assisted particle swarm optimization framework for expensive constrained optimization called CONOPUS (CONstrained Optimization by Particle swarm Using Surrogates). In each iteration, CONOPUS considers multiple trial positions for each particle in the swarm and uses surrogate models for the objective and constraint functions to identify the most promising trial position where the expensive functions are evaluated. Moreover, the current overall best position is refined by finding the minimum of the surrogate of the objective function within a neighborhood of that position and subject to surrogate inequality constraints with a small margin and with a distance requirement from all previously evaluated positions. CONOPUS is implemented using radial basis function (RBF) surrogates and the resulting algorithm compares favorably to alternative methods on 12 benchmark problems and on a large-scale application from the auto industry with 124 decision variables and 68 inequality constraints.

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Authors and Affiliations

  1. Saint Joseph’s University, Philadelphia, PA, 19131, USA

    Rommel G. Regis

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  1. Rommel G. Regis
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Correspondence to Rommel G. Regis .

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Editors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  2. University of Lille, Lille, France

    Nouredine Melab

  3. University of Lille, Lille, France

    El-Ghazali Talbi

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Regis, R.G. (2018). Surrogate-Assisted Particle Swarm with Local Search for Expensive Constrained Optimization. In: Korošec, P., Melab, N., Talbi, EG. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture Notes in Computer Science(), vol 10835. Springer, Cham. https://doi.org/10.1007/978-3-319-91641-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-91641-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91640-8

  • Online ISBN: 978-3-319-91641-5

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