6. Conclusions
In this paper, taking into consideration the effect of service routing on network service layer, transport layer, and physical topology layer risk, we created an integrated routing risk model, after which we proposed a controllable chaotic immune routing algorithm (CCIRA) in order to reduce the routing risk. Due to the inefficiency of traditional path generation methods, we proposed a path generation method based on chaotic search and dynamic adjacency matrix. This method can efficiently generate feasible solutions, improving the efficiency of routing optimization algorithms. In CCIRA, the use of a method that combines dynamic vaccination and free mutation ensures the convergence rate and global optimization capability. The use of chaotic search strategy instead of probability-based strategy during the path generation, vaccination, and free mutation stages improves the controllability and practicability of the algorithm because of the pseudorandomness, ergodicity and determinacy of chaotic sequences. On the LATAX and ITNA networks, the outstanding optimization performance of CCIRA is proved by the simulation results. The risk control performance of the combination of the integrated routing risk model and CCIRA is also proved to be superior by the comparison results with the other risk-aware routing algorithm. This paper conducted an exploratory study on the feasibility of using chaotic search strategy instead of probability-based strategy in evolutionary algorithms for routing problems, and can be used as a reference for future research on improving the controllability of evolutionary algorithms for routing problems.
