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A Swarm Algorithm Inspired by the Collective Animal Behavior

  • Erik CuevasEmail author
  • Fernando Fausto
  • Adrián González
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 160)

Abstract

In this chapter, a swarm algorithm for global optimization called the Collective Animal Behavior (CAB) is introduced. The algorithm is based on animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, that exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic and to avoid predation. In the presented algorithm in this chapter, the searcher agents emulate a group of animals which interact to each other based on the biological laws of collective motion. The optimization method presented in this chapter has been compared to other well-known optimization algorithms. The results, experiments and practical examples confirm the high performance of the presented method to find a global optimum of several benchmark functions.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Erik Cuevas
    • 1
    Email author
  • Fernando Fausto
    • 2
  • Adrián González
    • 3
  1. 1.CUCEI, Universidad de GuadalajaraGuadalajaraMexico
  2. 2.CUCEI, Universidad de GuadalajaraGuadalajaraMexico
  3. 3.CUCEI, Universidad de GuadalajaraGuadalajaraMexico

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