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Optimizing Support Vector Regression with Swarm Intelligence for Estimating the Concrete Compression Strength

  • Manoel Alves de Almeida NetoEmail author
  • Roberta de Andrade de A. FagundesEmail author
  • Carmelo J. A. Bastos-FilhoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)

Abstract

Estimating the compression strength of concrete is a complex problem which has been studied by various researchers. Support Vector Regression (SVR) is a technique that has been shown to be adequate for estimation through input examples. In this paper, we assess three swarm algorithms, Fish School Search (FSS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) aiming to optimize the SVR parameter. The results show that both all swarm-based algorithms far outperformed the original SVR in the concrete compression strength estimation task and the FSS and ABC obtained better results than PSO for this particular problem.

Keywords

Regression models Concrete compression strength Hybrid algorithms Swarm intelligence 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Pernambuco (UPE)RecifeBrazil

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