2 research outputs found
An efficient ant colony optimization strategy for the resolution of multi-class queries
Ant Colony Optimization is a bio-inspired computational technique for establishing optimal paths in
graphs. It has been successfully adapted to solve many classical computational problems, with considerable
results. Nevertheless, the attempts to apply ACO to the question of multidimensional problems
and multi-class resource querying have been somewhat limited. They suffer from either severely decreased
efficiency or low scalability, and are usually static, custom-made solutions with only one particular
use. In this paper we employ Angry Ant Framework, a multipheromone variant of Ant Colony System
that surpasses its predecessor in terms of convergence quality, to the question of multi-class resource
queries. To the best of the authors knowledge it is the only algorithm capable of dynamically creating
and pruning pheromone levels, which we refer to as dynamic pheromone stratification. In a series of experiments
we verify that, due to this pheromone level flexibility, Angry Ant Framework, as well as our
improvement of it called Entropic Angry Ant Framework, have significantly more potential for handling
multi-class resource queries than their single pheromone counterpart. Most notably, the tight coupling
between pheromone and resource classes enables convergence that is both better in quality and more
stable, while maintaining a sublinear cost.
© 2016 Elsevier B.V. All rights reserved.Kamil Krynicki is a FPI fellow of Universitat Politecnica de Valencia, number 3117. This work received support from Spanish Ministry of Economy and Competitiveness and European Development Regional Fund (EDRF-FEDER) with the project SUPEREMOS TIN2014-60077-R and the National Institute of Informatics, Tokyo, Japan.Krynicki, K.; Houle, ME.; Jaén Martínez, FJ. (2016). An efficient ant colony optimization strategy for the resolution of multi-class queries. Knowledge-Based Systems. 105:96-106. https://doi.org/10.1016/j.knosys.2016.05.009S9610610