Bi-Velocity Coevolutionary Multiswarm Particle Swarm Optimization for Many-Objective Gateway Placement Optimization

The gateway placement optimization (GPO) is a critical issue in satellite network that aims to obtain an optimal scheme to deploy different gateways in a network to achieve high-performed satellite-ground communication. Existing studies usually treat the GPO as a single-objective optimization problem, which significantly deviates from real-world scenarios and limits practical applicability. However, numerous performances like the gateway traffic load balancing, the distance between gateways, the traffic load of satellites, and the number of gateways within the satellite’s management range should be considered in the GPO problem, indicating that it is inherently a many-objective optimization problem (MaOP). Therefore, in this paper, a new mathematical model is designed which constructs the GPO as an MaOP. To address it, this paper further proposes a bi-velocity coevolutionary multiswarm particle swarm optimization (BCMPSO) algorithm. The BCMPSO follows the multiple populations for multiple objectives framework, running different populations in parallel, each optimizing a specific objective to search different parts of the Pareto front sufficiently. Meanwhile, a binary PSO with a bi-velocity update mechanism and a discrete position update mechanism is proposed as the optimizer in each population. Comparison experiments with several state-of-the-art methods confirm the effectiveness and competitiveness of the BCMPSO for many-objective GPO.