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Cooperation-Based Search of Global Optima

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Optimization and Learning (OLA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1684))

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Abstract

A new cooperation-based metaheuristic is proposed for searching gobal optima of functions. It is based on the assumption that the dynamics of the objective function does not change significantly between iterations. It relies on a local search process coupled with a cooperative semi-local search process. Its performances are compared against four other metaheuristics on unconstrained mono-objective optimization problems. Results show that the proposed metaheuristic is able to find the global minimum of the tested functions faster than the compared methods while reducing the number of iterations and the number of calls of the objective function.

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Authors and Affiliations

  1. IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, UT1, UT2, Toulouse, France

    Damien Vergnet, Elsy Kaddoum, Nicolas Verstaevel & Frédéric Amblard

Authors
  1. Damien Vergnet
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  2. Elsy Kaddoum
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  3. Nicolas Verstaevel
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  4. Frédéric Amblard
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Corresponding author

Correspondence to Damien Vergnet .

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Paris-Est, Paris, France

    Amir Nakib

  4. Université de Lille, Lille, France

    El-Ghazali Talbi

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Vergnet, D., Kaddoum, E., Verstaevel, N., Amblard, F. (2022). Cooperation-Based Search of Global Optima. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-22039-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22038-8

  • Online ISBN: 978-3-031-22039-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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