首页> 外文会议>Asia-Pacific Conference on Simulated Evolution and Learning(SEAL'2002); 20021118-22; Singapore(SG) >A GENETIC ALGORITHM FOR JOINT OPTIMIZATION OF SPARE CAPACITY AND DELAY IN SELF-HEALING NETWORK
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A GENETIC ALGORITHM FOR JOINT OPTIMIZATION OF SPARE CAPACITY AND DELAY IN SELF-HEALING NETWORK

机译:自愈网络中备用容量和延迟联合优化的遗传算法

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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).
机译:本章介绍了使用多目标遗传算法(mGA)解决使用虚拟路径(VP)概念设计自愈网络时出现的容量和路由分配问题。过去的研究表明,预先计划的备份保护方法和路径还原方案可以在保留的备用容量和故障恢复时间上做出很好的折衷。旨在最大程度地减少工作和备用容量的使用以及传输延迟的目的经常相互竞争并相互矛盾。多目标遗传算法是解决此类多目标问题的有力方法。在本章中,提出了一种多目标GA方法来实现上述两个目标,同时仍然可以满足一组客户流量需求,并且在单点故障下流量可以100%恢复。我们进行了一些实验,结果说明了目标之间的权衡,以及这种方法一次运行即可产生许多良好折衷解决方案的能力。为了衡量方法的性能,我们将结果与使用单目标遗传算法(sGA)进行比较。

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