Ant Colony Optimization has proven to be an important optimization technique. It has provided a solid base for solving classical computational problems, networks routing problems and many others. Nonetheless, algorithms within the Ant Colony metaheuristic have been shown to struggle to reach the global optimum of the search space, with only a few select ones guaranteed to reach it at all. On the other hand, Ant Colony based hybrid solutions that address this issue suffer from either severely decreased efficiency or low scalability and are usually static and custom-made, with only one particular use. In this paper we present a generic and robust solution to this problem, restricted rigorously to the Ant Colony Optimization paradigm, named Angry Ant Framework. It adds a new dimension -- a dynamic, biologically-inspired pheromone stratification, which we hope can become the objective of further state-of-the-art research. We present a series of experiments to enable a discussion on the benefits provided by this new framework. In particular, we show that Angry Ant Framework increases the efficiency, while at the same time improving the flexibility, the adaptability and the scalability with a very low computational investment.
['Kamil Krynicki', 'Michael E. Houle', 'Javier Jaen']
A Non-hybrid Ant Colony Optimization Heuristic for Convergence Quality