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The Metaheuristic Algorithm of the Social-Spider

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Advances in Metaheuristics Algorithms: Methods and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 775))

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Abstract

Metaheuristic is a computer science field which emulates the cooperative behavior of natural systems such as insects or animals. Many methods resulting from these models have been suggested to solve several complex optimization problems. In this chapter, a metaheuristic approach known as the Social Spider Optimization (SSO) is analyzed for solving optimization problems. The SSO method considers the simulation of the collective operation of social-spiders. In SSO, candidate solutions represent a set of spiders which interacts among them based on the natural laws of the colony. The algorithm examines two different kinds of search agents (spiders): males and females. According to the gender, each element is conducted by a set of different operations which imitate different behaviors that are commonly observed in the colony.

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Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas, Daniel Zaldívar & Marco Pérez-Cisneros

Authors
  1. Erik Cuevas
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  2. Daniel Zaldívar
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  3. Marco Pérez-Cisneros
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Corresponding author

Correspondence to Erik Cuevas .

Appendix: List of Benchmark Functions

Appendix: List of Benchmark Functions

See Table 2.4.

Table 2.4 Test functions used in the experimental study
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Cuevas, E., Zaldívar, D., Pérez-Cisneros, M. (2018). The Metaheuristic Algorithm of the Social-Spider. In: Advances in Metaheuristics Algorithms: Methods and Applications. Studies in Computational Intelligence, vol 775. Springer, Cham. https://doi.org/10.1007/978-3-319-89309-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-89309-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89308-2

  • Online ISBN: 978-3-319-89309-9

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