Ant colony meta-heuristics - Schemes and software framework

Abstract

Ant Colony Optimization is a strain of swarm intelligence algorithm, exploiting the foraging behavior of ants. Ants individually are sub-intelligent species, but share information using a chemical called pheromone that allows the colony to seek out optimal amount of food. ACO had efficiently been utilized to solve many NP-hard problems. The nature of the algorithm is such that it is extremely suited to solve assignment type problems, commonly a feature of combinatorial and assignment optimization problems. However, ACO by itself tend to be less powerful, due in part to redundant solution construction cycles. Hence, ACO integrates with another local search heuristics to achieve good results. This motivates a need for an ACO software framework, which forms the primary objective of this thesis. Aside from the software framework, the secondary objective of the thesis explores various factors of the ACO algorithm that are exploitable to achieve efficient results for complex problems.