@inproceedings{5372a9a979764a079fbfcfe60e8223ba,
title = "A Genetic Algorithm for Joint Optimization of spare Capacity and delay in Self-Healing Network",
abstract = "This chapter presents the use of multi-objective Genetic Algorithms (mGA) to solve the capacity and routing assignment problem arising in the design of self-healing networks using the Virtual Path (VP) concept. Past research has revealed that Pre-planned Backup Protection method and the Path Restoration scheme can provide a good compromise on the reserved spare capacity and the failure restoration time. The aims to minimize the sum of working and backup capacity usage and transmission delay often compete and contradict with each other. Multi-objective Genetic algorithm is a powerful method for this kind of multi-objective problems. In this chapter, a multi-objective GA approach is proposed to achieve the above two objectives while a set of customer traffic demands can still be satisfied and the traffic is 100% restorable under a single point of failure. We carried out a few experiments and the results illustrate the trade-off between objectives and the ability of this approach to produce many good compromise solutions in a single run. To measure the performance of approach, our results are used to compare with that using single objective genetic algorithm (sGA).",
author = "Sam KWONG and CHONG, {H. W.}",
year = "2004",
doi = "10.1142/9789812561794_0029",
language = "English",
isbn = "9789812389527",
series = "Advances in Natural Computation",
publisher = "World Scientific",
pages = "542--561",
editor = "TAN, {Kay Chen } and LIM, {Meng Hiot } and YAO, {Xin } and WANG, {Lipo }",
booktitle = "Recent Advances in Simulated Evolution and Learning",
address = "Singapore",
}