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Towards Applying River Formation Dynamics in Continuous Optimization Problems

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

River Formation Dynamics (RFD) is a metaheuristic that has been successfully used by different research groups to deal with a wide variety of discrete combinatorial optimization problems. However, no attempt has been done to adapt it to continuous optimization domains. In this paper we propose a first approach to obtain such objective, and we evaluate its usefulness by comparing RFD results against those obtained by other more mature metaheuristics for continuous domains. In particular, we compare with the results obtained by Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, and Social Spider Optimization.

This work has been partially supported by Spanish project TIN2015-67522-C3-3-R, and by Comunidad de Madrid as part of the program S2018/TCS-4339 (BLOQUES-CM) co-funded by EIE Funds of the European Union.

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Acknowledgments

The authors would like to thank Alberto de la Encina for valuable suggestions about the development of a version of RFD to deal with continuous domain optimization problems.

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Authors and Affiliations

  1. Dpto. Sistemas Informáticos y Programación, Facultad de Informática, Universidad Complutense de Madrid, C/ Prof José García Santesmases, 28040, Madrid, Spain

    Pablo Rabanal, Ismael Rodríguez & Fernando Rubio

Authors
  1. Pablo Rabanal
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  2. Ismael Rodríguez
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  3. Fernando Rubio
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Corresponding author

Correspondence to Fernando Rubio .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Rabanal, P., Rodríguez, I., Rubio, F. (2019). Towards Applying River Formation Dynamics in Continuous Optimization Problems. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_68

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20517-1

  • Online ISBN: 978-3-030-20518-8

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