Routing of multipoint connections
Broadband switching
SIAM Journal on Computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Ant Colony Optimization
BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Conditions that impact the complexity of QoS routing
IEEE/ACM Transactions on Networking (TON)
Ant colony optimization theory: a survey
Theoretical Computer Science
Design patterns from biology for distributed computing
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A survey of autonomic communications
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Data Structures, Algorithms, And Applications In C++
Data Structures, Algorithms, And Applications In C++
A bio-inspired architecture for division of labour in SANETs
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
Design and performance analysis of an inductive QoS routing algorithm
Computer Communications
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
QoS swarm state dependent routing for irregular traffic in telecommunication networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Bio-inspired networking: from theory to practice
IEEE Communications Magazine
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This article proposes a state-dependent routing algorithm based on a global optimization cost function whose parameters are learned from the real-time state of the network with no a priori model. The proposed approach samples, estimates, and builds the model of pertinent and important aspects of the network environment such as type of traffic, QoS policies, resources, etc. It is based on the trial/error paradigm combined with swarm-adaptive approaches. The global system uses a model that combines both a stochastic planned prenavigation for the exploration phase with a deterministic approach for the backward phase. We conducted a performance analysis of the proposed algorithm using OPNET based on several topologies such as the Nippon telephone and telegraph network. The simulation results obtained demonstrate substantial performance improvements over traditional routing approaches as well as the benefits of learning approaches for networks with dynamically changing traffic.