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Bio-inspired Computation in Unmanned Aerial Vehicles pp 35–69Cite as

Bio-inspired Computation Algorithms

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Abstract

Bio-inspired computation is the use of computers to model the living phenomena and simultaneously the study of life to improve the usage of computers. Swarm behaviors in animal groups such as bird flocks, bees, ants, fish schools, and sheep herds, as well as insects like mosquitoes, ants, and bees, often exhibit incredible abilities to solve complex problems that seem far beyond their capabilities. This chapter mainly focuses on the biological inspiration, principle, and implementation procedures of four popular bio-inspired computation algorithms including ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). Special emphasis has been laid on how the biological behavior can be transferred into a technical algorithm. Moreover, description of algorithms in more general terms and the most successful variants of these algorithms are provided. Finally, a brief introduction to other bio-inspired computation algorithms such as glowworm swarm optimization (GSM), bacteria foraging optimization (BFO), bat-inspired algorithm (BA) is presented.

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Authors and Affiliations

  1. Beihang University (BUAA), Beijing, People’s Republic of China

    Pei Li

  2. Beihang University (formerly Beijing University of Aeronautics and Astronautics, BUAA), Beijing, People’s Republic of China

    Haibin Duan

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  1. Pei Li
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  2. Haibin Duan
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Li, P., Duan, H. (2014). Bio-inspired Computation Algorithms. In: Bio-inspired Computation in Unmanned Aerial Vehicles. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41196-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-41196-0_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41195-3

  • Online ISBN: 978-3-642-41196-0

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