Ant-based load balancing in telecommunications networks
Adaptive Behavior
Mobile networking in the Internet
Mobile Networks and Applications - Special issue: mobile networking in the Internet
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Ants: agents on networks, trees, and subgraphs
Future Generation Computer Systems
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Efficiently searching a graph by a smell-oriented vertex process
Annals of Mathematics and Artificial Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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A novel method for topology discovery of computer network using ant colony optimisation ACO is proposed. In this approach, the base station BS simulates the way ants forage for food to find out the routes to other BSs. The route discovered by each ant is associated with pheromone strength which in turn decides whether the route is the best or not. ACO applied to CN gives all the existing routes between the various BSs in a CN. Genetic algorithm GA-based optimisation is further applied to get the optimal path satisfying multiple constraints viz., number of hops, Poisson traffic distribution, buffer capacity, link delay, queuing delay, and residual bandwidth from the set of paths given by ACO. Our simulation results show that different network models viz., random model, unidirectional ring, bidirectional ring, star, and tree are explored faster using ACO and our scheme using ACO-GA outperforms the scheme without GA.