QoS routing in ad-hoc networks using GA and multi-objective optimization
QoS routing in ad-hoc networks using GA and multi-objective optimization
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DOI：
10.3233/mis-2011-0116
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发表时间：
2011-07
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通讯作者：
Admir Barolli;E. Spaho;L. Barolli;F. Xhafa;M. Takizawa
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作者：
Admir Barolli;E. Spaho;L. Barolli;F. Xhafa;M. Takizawa
在Ad-hoc网络中的路由方面已经做了很多工作，但是所提出的路由解决方案只能处理最好的数据流量。不支持对业务质量有QoS要求的连接，例如有延迟和带宽限制的语音通道。QoS路由问题受到越来越多的关注，但在多度量条件下寻找最短路径是一个np完全问题。因此，对于多路径约束的QoS路由，需要开发近似解和启发式算法。路由方法应具有适应性、灵活性和智能性。本文将遗传算法和多目标优化算法应用于自组织网络中的QoS路由。为了减小遗传算法的搜索空间，实现了一种搜索空间缩减算法，减少了基于GAMAN遗传算法的移动自组网路由算法寻找新路由的搜索空间。通过计算机仿真对GAMAN算法的性能进行了评价，结果表明GAMAN算法具有比GLBR遗传负载均衡路由更好的性能。
Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service QoS requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms GAs and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN GA-based routing algorithm for Mobile Ad-hoc Networks to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR Genetic Load Balancing Routing.