HYBRID FUZZY-GENETIC APPROACH FOR ROUTE OPTIMISATION IN MANETS

Research about Mobile Ad Hoc Networks (MANETs) intensifies because of their independent operation and wide functionality that includes military uses along with disaster recovery networks and automotive settings. The dynamic nature of their network topologies together with sparse resources leads to routing becoming an enduring complex issue. Standard networking standards fail to handle quick network topology changes effectively that results in reduced quality network delivery and longer delays and increased power usage. The research introduces the HFGA (Hybrid Fuzzy-Genetic Approach) that unites fuzzy logic with genetic algorithms to optimize route decisions through real-time adaptation. Through the fuzzy component the network evaluates energy levels of nodes and stability of links and queue lengths for making suitable node and link assessments that guide the genetic algorithm to generate optimal multi-hop paths via natural selection principles. NS-3 simulations show that HFGA delivers enhanced performance compared to AODV and DSR since it delivers higher packet delivery rates with minimized end-to-end delay and ensures balanced energy distribution along with longer network operational periods. The research adds important value to the development of intelligent routing mechanisms which meet next-generation wireless networks requirements.