Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

Introducing InterSatellite Links (ISLs) is a major trend in new-generation Global Navigation Satellite Systems (GNSSs). Data transmission scheduling is a crucial problem in the study of ISL management. The existing research on intersatellite data transmission has not considered the capacities of ISL bandwidth. Thus, the current study is the first to describe the intersatellite data transmission scheduling problem with capacity restrictions in GNSSs. A model conversion strategy is designed to model the aforementioned problem as a length-bounded single-path multicommodity flow problem. An integer programming model is constructed to minimize the maximal sum of flows on each intersatellite edge; this minimization is equivalent to minimizing the maximal occupied ISL bandwidth. An iterated tree search algorithm is proposed to resolve the problem, and two ranking rules are designed to guide the search. Experiments based on the BeiDou satellite constellation are designed, and results demonstrate the effectiveness of the proposed model and algorithm.