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<p>In this paper, a reliable and efficient data routing scheme is developed that employs the conventional AODV protocol based on dynamic genetic algorithm. It is aimed at effectively allocating the scarce radio resources and improving QoS among the wireless devices in MANETs by joint optimization of network attributes including the data transfer rate, link transmission power and round trip delay. The proposed genetic algorithm-based routing scheme utilizes two different fitness functions, together with binary data coding and decoding, single-point crossover and random mutation operators to assess the fitness measure of specific solution space and network operational characteristics. This computational learning method is trained through sample dataset obtained via simulation experiments of the basic AODV routing. Finally, our smart network data learning and genetic optimization model is compared with previous related models to demonstrate its improved performance in terms of lower power consumption, higher throughput, and greater average fitness measure.</p>

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Multiple-Criteria Fitness Function Based Genetic Optimization of AODV Routing Protocol in MANETs

  • 期刊论文

  • 发表日期：

    2025年2月14日

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  • Ridhima Mehta

摘要

In this paper, a reliable and efficient data routing scheme is developed that employs the conventional AODV protocol based on dynamic genetic algorithm. It is aimed at effectively allocating the scarce radio resources and improving QoS among the wireless devices in MANETs by joint optimization of network attributes including the data transfer rate, link transmission power and round trip delay. The proposed genetic algorithm-based routing scheme utilizes two different fitness functions, together with binary data coding and decoding, single-point crossover and random mutation operators to assess the fitness measure of specific solution space and network operational characteristics. This computational learning method is trained through sample dataset obtained via simulation experiments of the basic AODV routing. Finally, our smart network data learning and genetic optimization model is compared with previous related models to demonstrate its improved performance in terms of lower power consumption, higher throughput, and greater average fitness measure.