Heuristics for Evolutionary Off-line Routing in Telecommunication Networks

Off-line routing in backbone telecommunications networks is a combinatorial optimization problem well-suited to iterative search methods. In this paper, a number of further heuristics applicable to the routing problem are introduced and evaluated. The presented results show that these methods significantly improve the search for solutions, particularly when on-line performance is considered. The use of delta-evaluation of solutions in order to reduce computation time further, is also investigated. Previously, simulated annealing has had a significant advantage over genetic algorithms on problems where delta-evaluation is applicable, because genetic algorithms employing standard crossover operators cannot make use of the technique. However, we introduce a specialized recombination operator which enables a genetic algorithm to exploit delta-evaluation effectively on the routing problem. The performance of the genetic algorithm employing the new recombination operator is found to be significantly better than that of a mutation-only genetic algorithm.

[1]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[2]  Subhash C. Narula,et al.  Degree-constrained minimum spanning tree , 1980, Comput. Oper. Res..

[3]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[4]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Andrew S. Tanenbaum,et al.  Computer networks, third edition , 1996 .

[6]  Martin J. Oates,et al.  QoS based GA Parameter Selection for Autonomously Managed Distributed Information Systems , 1998, ECAI.

[7]  Robin J. Wilson.  Graphs : an introductory approach : a first course in discrete mathematics / Robin J. Wilson, John J. Watkins , 1989 .

[8]  Robin Wilson,et al.  Graphs an Introductory Approach , 1990, The Mathematical Gazette.

[9]  Peter Ross,et al.  Useful diversity via multiploidy , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[10]  Martin J. Oates,et al.  Investigating Evolutionary Approaches to Adaptive Database Management Against Various Quality of Service Metrics , 1998, PPSN.

[11]  David E. Goldberg,et al.  Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy , 1987, ICGA.

[12]  David Corne,et al.  Evolutionary Approaches to Off-Line Routing in Backbone Communications Networks , 1998 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  G. Bilchev,et al.  Comparing genetic algorithms and greedy heuristics for adaptation problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[15]  Masaharu Munetomo,et al.  An Adaptive Network Routing Algorithm Employing Path Genetic Operators , 1997, ICGA.

[16]  Dipankar Dasgupta,et al.  SGA: A Structured Genetic Algorithm , 1992 .

[17]  Yukiko Yoshida,et al.  A Diploid Genetic Algorithm for Preserving Population Diversity - pseudo-Meiosis GA , 1994, PPSN.