ACO-GA Hybrid MetaHeuristic ( AGHM ) Optimization for Multi-constrained Quality of Service Routing in MANETs

In MANET, designing a dynamic routing algorithm by satisfying QoS requirement is a challenging task. Additionally, multi-constrained QoS routing aims to optimize multiple QoS metrics while provisioning required network resources and is an admittedly complex problem. It has been proved to be NP-complete when a combination of additive, concave and multiplicative metrics are considered. Hence this problem can be solved using stochastic optimization methods like ACO and GA. In Genetic Algorithm (GA), a population of candidate solutions to an optimization problem is evolved towards better solutions. But GA doesn’t confirm with an exhaustive exploration while constructing initial candidate solutions. Ant Colony Optimization (ACO) technique matches the routing requirements of Mobile Ad-hoc Networks because of its motivating properties like foraging and self-organising nature. The proposed ACO GA Hybrid Meta-heuristic (AGHM) approach aims to utilize the benefits of the two meta-heuristic techniques as a combined approach in order to reduce the complexities in the dynamic environment. AGHM uses the foraging quality of ACO to construct the initial candidate solutions which includes all possible paths that satisfies required QoS. Then it employs the evolutionary nature of GA to construct best solutions for multi-path multi-constraint QoS routing. After due investigation, it has been showed that the proposed hybrid approach improves the performance of MANET routing with satisfied QoS requirements.