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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Bio-inspired Computation Algorithms


    Contributors:
    Li, Pei (author) / Duan, Haibin (author)


    Publication date :

    2013-09-30


    Size :

    35 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Bio-inspired computation in Unmanned Aerial Vehicles

    Duan, Haibin / Li, Pei | TIBKAT | 2014


    Brain-inspired algorithms for retinal image analysis

    Haar Romeny, B. M. | British Library Online Contents | 2016


    Research Status and Development Trends of the Bio-inspired Computation

    Zhu, Yunlong / Shen, Hai / Chen, Hanning et al. | British Library Online Contents | 2016



    Energy-Efficient Train Operation Using Nature-Inspired Algorithms

    Kemal Keskin / Abdurrahman Karamancioglu | DOAJ | 2017

    Free access