Affiliation:
1. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Switzerland
Abstract
Modern telecommunication networks are becoming increasingly complex and dynamic. This is due to their size and heterogeneity, and to the complex interactions among their elements. Classical techniques for network control were not conceived to face such challenges. Therefore, new algorithms are needed that are adaptive and robust, work in a self-organized and decentralized way, and are able to cope with heterogeneous large-scale systems. In this chapter, we argue that the reverse engineering of natural processes can provide a fruitful source of inspiration for the design of such algorithms. In particular, we advocate the use of ant colony optimization (ACO), an optimization metaheuristic inspired by the foraging behavior of ant colonies. We discuss the characteristics of ACO and derive from it ant colony routing (ACR), a novel framework for the development of adaptive algorithms for network routing. We show through the concrete application of ACR’s ideas to the design of an algorithm for mobile ad hoc networks that the ACR framework allows the relatively straightforward construction of new routing algorithms that can deal with all of the challenges mentioned above.
Cited by
5 articles.
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1. Ant Colony Optimization Beats Resampling on Noisy Functions;Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion;2016-07-20
2. Robustness of Ant Colony Optimization to Noise;Evolutionary Computation;2016-06
3. Robustness of Ant Colony Optimization to Noise;Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation;2015-07-11
4. A Particle Swarm Optimization Algorithm for the Multicast Routing Problem;Models, Algorithms and Technologies for Network Analysis;2014
5. Principles and applications of swarm intelligence for adaptive routing in telecommunications networks;Swarm Intelligence;2010-03-05