Hybrid Glowworm Swarm Optimization (HGSO) agent for Qos based Routing in Wireless Networks

Nowadays, the rapidly developing science of services focuses on service management, which has to take into account the available resources and the user's wishes concerning the desired quality and costs. Quality of Service (QoS) management is perceived as a special aspect of distributed systems management in WSN. Providing QoS aware routing is a challenging task in this type of network due to dynamic topology and limited resources. The main purpose of QoS aware routing is to find a feasible path from source to destination which will satisfy two or more end to end QoS constrains. Therefore, the task of designing an efficient routing algorithm which will satisfy all the quality of service requirements and be robust and adaptive is considered as a highly challenging problem. Intelligent software agents are employed to monitor the changes that occur in network structure, network communication flow and each node’s routing state. These agents can after that participates in network routing and network maintenance. Swarm intelligence techniques have been extensively used in optimization problem for solving many optimization issues in WSNs. To solve this problem previously Hybrid Genetic Firefly Algorithm (HFGA) was proposed which can be used for solving optimization problems. However, parameters are set fixed and they do not change with the time. In addition Firefly algorithm does not memorize or remember any history of better situation for each firefly and this causes them to move regardless of its previous better situation, and they may end up missing their situations. In order to avoid this problem, in this work a new efficient and energy aware multipath routing algorithm designed called as a novel hybrid glowworm swarm optimization (HGSO) algorithm. The presented algorithm embeds predatory behavior of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm. This proposed Routing Algorithm will increase network lifetime and decrease packet loss and average end to end delay that makes this algorithm suitable for real time and multimedia applications. The results show that HGSO algorithm has faster convergence speed, higher computational precision, and is more effective for solving constrained engineering design problems.