A controllable chaotic immune algorithm for risk-aware routing in DiffServ networks

An integrated routing risk model is constructed, which takes into account the effects of unicast routing on DiffServ network risk consisting of the impacts of interrupted services on network users and path availability. With the objective of minimizing integrated routing risk, a novel controllable chaotic immune routing algorithm (CCIRA) is proposed. Due to the inefficiency of traditional path generation methods, a path generation method based on chaotic search and dynamic adjacency matrix is proposed, improving the generation efficiency of available solutions of routing optimization algorithms. An evolutionary strategy which combines dynamic vaccination and free mutation is used in order to ensure the population diversity and the global convergence of CCIRA. Chaotic search is introduced to population initialization, vaccination and free mutation in order to overcome the uncertainty of the optimization process and optimization results in traditional evolutionary algorithms due to the crossover and mutation strategies being based on random numbers. Simulation results prove that CCIRA is highly efficient and practical. Combining the integrated routing risk model and CCIRA, the risk control performance of our risk-aware routing algorithm is also proved to be superior by the comparison with other algorithms.