Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Simulated annealing and combinatorial optimization
DAC '86 Proceedings of the 23rd ACM/IEEE Design Automation Conference
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The Vision of Autonomic Computing
Computer
An Agent-Based Framework for Nomadic Computing
FTDCS '99 Proceedings of the 7th IEEE Workshop on Future Trends of Distributed Computing Systems
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Progressive Stochastic Search for Solving Constraint Satisfaction Problems
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Ant Colony Optimization
iPass: An Incentive Compatible Auction Scheme to Enable Packet Forwarding Service in MANET
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Communications and Control--A Natural Linkage for SWARM
Journal of Network and Systems Management
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
IEEE Journal on Selected Areas in Communications
Self-organizing network services with evolutionary adaptation
IEEE Transactions on Neural Networks
Towards a Management Paradigm with a Constrained Benchmark for Autonomic Communications
Computational Intelligence and Security
A multi-agent self-adaptative management framework
International Journal of Network Management
Adaptive real-time monitoring for large-scale networked systems
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Ant-based topology convergence algorithms for resource management in VANETs
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Ant-based service selection framework for a smart home monitoring environment
Multimedia Tools and Applications
Hi-index | 0.00 |
This paper proposes a self-organizing scheme based on ant metaheuristics to optimize the operation of multiple classes of managed elements on an Operations Support Systems (OSSs) for mobile pervasive communications. Ant metaheuristics are characterized by learning and adaptation capabilities against dynamic environment changes and uncertainties. As an important division of swarm agent intelligence, it distinguishes itself from centralized management schemes due to its features of robustness and scalability. We have successfully applied ant metaheuristics to the network service configuration process, which is simply redefined as: the managed elements represented as graphic nodes, and ants traverse by selecting nodes with the minimum cost constraints until the eligible network elements are located along near-optimal paths--the located elements are those needed for the configuration or activation of a particular product and service. Although the configuration process is non-transparent to end users, the negotiated SLAs between users and providers affect the overall process. This proposed self-organized learning and adaptation scheme using Ant Colony Optimization (ACO) is evaluated by simulation in Java. A performance comparison is also made with a class of Genetic Algorithm known as PBIL. Finally, the simulation results show the scalability and robustness capability of autonomous ant-like agents able to adapt to dynamic networks.