Discrete history ant systems

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.