A Model For Self -Adaptive Routing Optimization in Mobile Ad-hoc Network.

In Mobile Ad-Hoc Network, unpredictable topology is one of the challenges that can degrade its routing performance. Particle Swarm Optimization (PSO) is a technique that has a challenge of converging prematurely when large numbers of intermediate nodes are on the network. This study designed, simulate and access the performance of Self-Adaptive Partitioned PSO (SAP-PSO) routing model in a Mobile Ad-Hoc Network (MANET). The proposed model sends information from source to destination in a 1-hop neighbor network, performed the routing optimization technique of existing PSO when numbers of intermediate nodes are few (less than one equal to ten nodes (greater than 10) are on the network, the model automatically grouped nodes into partition, and this reduces the rate of premature convergences. Input data was generated using uniformly distributed random number. The model was bench marked with the exiting POS using global best and computational time as performance metrics and simulation was carried out in MATLAB7.0 programming environment. The simulation result considered scenarios when the number of intermediate nodes is than 10 and when it is greater than 10.