Ant colony optimisation: from biological inspiration to an algorithmic framework

The Ant Colony Optimisation algorithm framework here-on referred to as ACO is a new algorithmic framework which is inspired by the foraging patterns of biological ants. Any ACO algorithm (of which there are many) serves to optimise some problem instance by generating a series of solutions to the problem and using the utility (goodness) of these solutions to influence future solution construction. This report outlines the biological inspiration behind the development of the first ant-inspired algorithms. The report then identifies two of these ant-inspired algorithms, their relation to the biological models and offers a contrast and comparison between them. Finally the report describes and analyses the ACO meta-heuristic framework to which a subset of ant-inspired algorithms belong. The report is organised as follows. Section 2 describes the recruitment and foraging behaviour of four species of ants. Section 3 identifies two ant-inspired algorithms: Ant Systems (AS) and ant colony optimisation for continuous design spaces. Finally Section 4 defines the ACO meta-heuristic framework and comments on the properties of the framework and it’s relation to ant-inspired algorithms. [Introduction]