0000000000855008
AUTHOR
Guro ØDesneltvedt
Optimizing PolyACO Training with GPU-Based Parallelization
A central part of Ant Colony Optimisation (ACO) is the function calculating the quality and cost of solutions, such as the distance of a potential ant route. This cost function is used to deposit an opportune amount of pheromones to achieve an apt convergence, and in an active ACO implementation a significant part of the runtime is spent in this part of the code. In some cases, the cost function accumulates up towards 94 % in its run time making it a performance bottle neck.
PolyACO+: a multi-level polygon-based ant colony optimisation classifier
Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof of concept polygon-based classifier that resorts to ant colony optimisation as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classific…