Near-Pareto Multiobjective Routing Optimization for Space–Air–Sea-Integrated Networks

The communication among nodes in the space–air–sea integrated network (SASIN) relies on collaborative multihop transmission. Hence, effective routing techniques should be designed to optimize multiple indicators. Routing optimization for multihop is usually focused on optimizing a single metric. Moreover, designing effective routing strategies for multihop networks with SASIN is challenging as balancing multiple performance metrics can lead to conflicts. In this article, we propose near-Pareto multiobjective routing optimization for SASIN, which adopts multiobjective combinatorial optimization (MOCOP) to strike a tradeoff among multiple objectives. We establish the SASIN system model, including channel models of communication links between satellites, aircraft, and ships. Furthermore, we use multiobjective optimization methods to formulate objective functions of spectral efficiency, energy efficiency, and delay. We employ the multiobjective evolutionary algorithms (MOEAs) for approximating the set of the Pareto optimal solutions. An improved nondominated sorting genetic algorithm II (INSGA II) and an improved strength Pareto evolutionary algorithm II (ISPEA II) are proposed to generate approximations of the Pareto optimal set. We evaluated the MOCOP formulation, and the SASIN network topology was built based on real data and simulated data. The simulation results indicate that a set of beneficial tradeoff solutions can be obtained for providing flexible selection of communication connections by addressing the multiobjective routing problem formulated. The results demonstrate that the MOEAs utilized have the potential to find Pareto-optimal solutions for SASIN.