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Swarm Intelligence: A Review of Optimization Algorithms Based on Animal Behavior

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Recent Advances of Hybrid Intelligent Systems Based on Soft Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 915))

Abstract

In this paper a review about the optimization algorithms based on swarm intelligence (SI) with animal behavior is presented. In this review, are analyzed the most popular algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) and the Bat algorithm (BA). These algorithms are mentioned in the paper because they inspired on animal behavior demonstrating be useful for solving optimization problems in several applications, and also the algorithms are inspired in swarm intelligence and share similarities in some aspects.

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Acknowledgements

We would like to express our gratitude to CONACYT, Tecnologico Nacional de Mexico/Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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  1. Computer Science, Tijuana Institute of Technology, Calzada Tecnologico S/N, Tijuana, 22450, BC, Mexico

    Fevrier Valdez

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  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Valdez, F. (2021). Swarm Intelligence: A Review of Optimization Algorithms Based on Animal Behavior. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Recent Advances of Hybrid Intelligent Systems Based on Soft Computing. Studies in Computational Intelligence, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-58728-4_16

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