Swarm Based Hybrid ACO-PSO Meta-Heuristic (HAPM) for QoS Multicast Routing Optimization in MANETs

For large and dynamic networks, traditional MANETs multicast routing protocols are not appropriate for searching the optimal paths considering QoS constraints as the problem leads to NP-complete in nature. Biologically inspired algorithms like Ant colony optimization (ACO), Particle swarm optimization (PSO) and Artificial Bee Colony have attracted great attention from researchers to solve the combinatorial problem. ACO and PSO provide more reliable routes as compared to traditional methods. In this paper, we have proposed 'Hybrid ACO-PSO Meta-Heuristic (HAPM)', a combination of ACO, PSO, and a dynamic queue mechanism to improve QoS constraints and minimize QoS the data dropping. Simulation is performed in NS2 and the results revealed that the presented HAPM algorithm provides better efficiency in terms of Packet Delivery Ratio (PDR), Ent-to-End Delay, Hop Count (H_c), Routing Overhead) and Throughput as compared to ACO, PSO, hybrid ACO-PSO, Enhanced-Ant-AODV and Cuckoo Search Optimization AODV (CSO-AODV). The PDR in HAPM is improved by 20%, 11%, 8%, 2% and 0.6%; delay of HAPM is reduced by 54%, 47%, 40%, 49%, and 30%; routing overhead of HAPM is reduced by 49%, 41%, 23%, 10% and 17%; throughput of HAPM is improved by 40%, 28%, 11%, 8% and 36% as compared to ACO, PSO, hybrid ACO-PSO, Enhanced-Ant-AODV and Cuckoo Search Optimization AODV (CSO-AODV) respectively. The Hop count of HAPM has also been reduced by 90%, 87%, and 83% compared to ACO, PSO, and Enhanced-Ant-AODV respectively. The proposed HAPM does not overburden the time complexity in our implementation.