This chapter describes the joint application of two soft computing methods – evolutionary algorithms and fuzzy reasoning – to the problem of adaptive distributed routing control in packet-switched communication networks. In this problem, a collection of geographically distributed routing nodes are required to adaptively route data packets so as to minimise mean network packet delay. Nodes reach routing decisions locally using state measurements which are delayed and necessarily only available at discrete sampling intervals. . | Telecommunications Optimization Heuristic and Adaptive Techniques. Edited by David W. Corne Martin J. Oates George D. Smith Copyright 2000 John Wiley Sons Ltd ISBNs 0-471-98855-3 Hardback 0-470-84163X Electronic 8 Routing Control in Packet Switched Networks using Soft Computing Techniques Brian Carse Terence C. Fogarty and Alistair Munro Introduction This chapter describes the joint application of two soft computing methods - evolutionary algorithms and fuzzy reasoning - to the problem of adaptive distributed routing control in packet-switched communication networks. In this problem a collection of geographically distributed routing nodes are required to adaptively route data packets so as to minimise mean network packet delay. Nodes reach routing decisions locally using state measurements which are delayed and necessarily only available at discrete sampling intervals. Interactions between different nodes routing decisions are strong and highly non-linear. Extant routing methods in packet-switched networks Khanna and Zinky 1989 mostly employ in one form or another some direct form of least-cost or shortest-path algorithm Dijkstra 1959 operating at each routing node. Such methods pay little attention to the dynamic interactions of routing decisions made at different nodes and do not directly address the temporal effects of delayed measurements and persistence of the effects of routing choices. This contribution proposes a very different approach to the routing problem. The routing policy of routing nodes is determined by a fuzzy rule base which takes as inputs various network state measurements and provides route selection probabilities as outputs. Telecommunications Optimization Heuristic and Adaptive Techniques edited by . Corne . Oates and . Smith 2000 John Wiley Sons Ltd. 138 Telecommunications Optimization Heuristic and Adaptive Techniques A Genetic Algorithm GA is used to optimise these fuzzy rule bases. Ait each generation of the GA identical .
