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An Artificial Orca Algorithm for Continuous Problems

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Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

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Abstract

In this paper, a very interesting species in its behaviour, social organization and also hunting/foraging techniques is mimicked in a new swarm intelligence algorithm. Several observations of Orcas in their living environment allows to model this species in a swarm optimization algorithm called Artificial Orca Algorithm (AOA). The algorithm proposes an original structure for its population and is modelled mathematically mimicking orca’s lifestyle on the intensification and diversification searches. The experimental study we conducted was tested on a benchmark used to evaluate the new swarm algorithm in a set of 21 mathematical optimization benchmark. The obtained results compared to GA(Genetic Algorithm), BA(Bat Algorithm), EHO(Elephant Herding Optimization), PSO(Particle Swarm Optimization) and WOA(Whale Optimization Algorithm) show that the proposed approach is efficient, which prompt to look further and test it on other optimization problem in order to benefit from the robustness of this latter. The source code of AOA is publicly available at: https://lria.usthb.dz/aoa.

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Notes

  1. 1.

    Permanent movement an individual makes from its birth site to the place where it reproduces or would have reproduced if it had survived.

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Author information

Authors and Affiliations

  1. Laboratory for Research in Artificial Intelligence LRIA, USTHB, Algiers, Algeria

    Lydia Sonia Bendimerad & Habiba Drias

Authors
  1. Lydia Sonia Bendimerad
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  2. Habiba Drias
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Corresponding author

Correspondence to Lydia Sonia Bendimerad .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Bendimerad, L.S., Drias, H. (2021). An Artificial Orca Algorithm for Continuous Problems. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_68

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