A novel hybrid immune-based GA for dynamic routing to multiple destinations for overlay networks

Overlay networks play an important role in group communication applications in Internet. These applications require better efficiency in terms of delay, cost and load balancing. This paper presents an artificial immune system (AIS)-based hybrid genetic algorithm for the construction of Quality of Service (QoS) multicast tree among multicast service nodes in overlay network which optimizes path delivery, load-balancing variance and cost under bounded delay–degree constraint. This paper proposes an alternative AIS-based approach to handle the constraints instead of penalty function in overlay multicast routing problem. The clonal selection method of AIS is incorporated into the genetic algorithm (GA) to improve the diversity–convergence relationship which leads to optimized results. Proposed algorithm has the following features: (1) embedded problem specific local search function along with random point crossover to fine tune the search; (2) AIS principle is used to solve the constraints in GA; (3) clonal selection method to get the optimized results. Adaptable procedure is embedded into algorithm to handle the end user join/end user drop. Non-parametric statistical analysis has performed to show the significant difference among the proposed and existing algorithms. Simulation results reveal that our proposed algorithm produces better results in terms of cost, average path length, user rejection rate and convergence. Statistical analysis is also performed to assure the significance of the differences among the tested algorithms.