Swinburne
Browse

Discrete history ant systems

Download (284.17 kB)
preprint
posted on 2024-07-13, 02:20 authored by Daniel Angus, Jason Brownlee
Ant Colony Optimisation (ACO) algorithms are inspired by the foraging behaviour of real ants and are a relatively new class of algorithm which have shown promise when applied to combinatorial optimisation problems. In recent years ACO algorithms have begun to gain popularity and as such are beginning to be applied to more complex problem domains including (but not limited to) dynamic problems. Recent modifications to the fundamental ACO algorithms such as population based approaches are enabling ACO algorithms to be competitive to other known biologically inspired search techniques in addressing these more complex problems. This paper outlines a foundation population based ACO algorithm which is imbued with characteristics that allow for multiple extensions beneficial in addressing more complex problems.

History

Copyright statement

Copyright © 2006 Daniel J. Angus and Jason Brownlee.

Language

eng

Usage metrics

    Other

    Categories

    No categories selected

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC