Agent-Based Modelling of Decentralized Ant Behaviour using High Performance Computing

Ant colonies are complex biological systems that respond to changing conditions in nature by solving dynamic problems. Their ability of decentralized decision-making and their self-organized trail systems have inspired computer scientists since 1990s, and consequently initiated a class of heuristic search algorithms, known as ant colony optimization (ACO) algorithms. These have proven to be very effective in solving combinatorial optimisation problems, especially in the field of telecommunication. The major challenge in social insect research is understanding how colony-level behaviour emerges from individual interactions. Models to date focus on simple pheromone usage with mathematically devised behaviour, which deviates largely from the real ant behaviour. Furthermore, simulating large-scale behaviour at the individual level is a difficult computational challenge; hence models fail to simulate realistic colony sizes and dimensions for foraging environments. In this thesis, FLAME, an agent-based modelling (ABM) framework capable of producing parallelisable models, was used as the modelling platform and simulations were performed on a High Performance Computing (HPC) grid. This enabled large-scale simulations of complex models to be run in parallel on a grid, without compromising on the time taken to attain results. Furthermore, the advanced features of the framework, such as dynamic creation of agents during a simulation, provided realistic grounds for modelling pheromones and the environment. ABM approach through FLAME was utilized to improve existing models of the Pharaoh’s ants (Monomorium pharaonis) focusing on their foraging strategies. Based on related biological research, a number of hypotheses were further tested, which were: (i) the ability of the specialist ‘U-turner’ ants in trail maintenance, (ii) the trail choices performed at bifurcations, and (iii) the ability of ants to deposit increased concentrations of pheromones based on food quality. Heterogeneous colonies with 7% U-turner ant agents were further shown to perform significantly better in foraging compared to homogeneous colonies. Furthermore, laying pheromones with a higher intensity based on food quality was shown to be beneficial for the Pharaoh’s ant colonies in switching to more rewarding trails. The movement of the Pharaoh’s ants in unexplored areas (without pheromones) was also investigated by conducting biological experiments. Video tracking was used to extract movement vectors from the recordings of experiments and the data obtained was subject to statistical analysis in order to devise parameters for ant movement in the models developed. Overall, this research makes contributions to biology and computer science research by: (i) utilizing ABM and HPC via FLAME to reduce technological challenges, (ii) further validating existing hypotheses through realistic models, (iii) developing a video tracking system to acquire experimental data, and (iv) discussing potential applications to emergent telecommunication and networking problems.