Abstract
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).
Highlights
The wireless mobile networks and devices are becoming increasingly popular and they provide users access to information and communication any-time and anywhere
We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than a previous method called Genetic Load Balancing Routing (GLBR)
When the mutation rate is 8%, the performance of proposed method is better than 10%
Summary
The wireless mobile networks and devices are becoming increasingly popular and they provide users access to information and communication any-time and anywhere. A. Barolli et al / QoS routing in ad-hoc networks using GA and multi-objective optimization have been proposed. Searching for the shortest path with minimal cost and finding delay constrained least-cost paths are NP-complete problems For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. In the case of mobile Ad-hoc networks, it is better to find a route very fast in order to have a good response time to the speed of topology change, than to search for the best route but without meaning, because the network topology is changed and this route does not exist any-more. We use GAs and multi-objective optimization for QoS routing in Ad-hoc Networks.
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