As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios.

As an indispensable part of the future 6G communication system, SIoT (Satellite Internet of Things) plays a vital role in global communication. However, the communication requirements of users, the manufacturing and launching costs of satellites, and the performance coverage of constellations pose significant challenges to the construction of SIoT. To facilitate the evolution of the 5G network into the 6G space-ground integrated network, this paper proposes a multi-area on-demand classification (MOC) constellation design based on CubeSats. Firstly, the area of interest is classified into two categories: the area with cellular base stations and the area without cellular base stations. Secondly, three coverage, communication quality, and cost models are established. The coverage model establishes the initial configuration of the constellation and determines the value for evaluating coverage. The communication model determines the evaluation values for communication quality. The cost model determines the evaluation values for cost. Finally, the multi-objective Manta ray foraging optimization algorithm (MOMRFO) optimizes the three evaluation values to obtain the Pareto front for the best configuration constellation. Based on actual data from the cellular base station, the simulation results have demonstrated the MOC constellation scheme’s feasibility and effectiveness for future SIoT deployment.

In closed Brayton cycle power generation systems, sudden load disturbances can induce a compressor surge in turbine–alternator–compressor systems, posing significant risks to dynamic stability and operational reliability. To address this challenge, this study proposes a PID control strategy optimized via a genetic algorithm. A high-fidelity dynamic model of the turbine–alternator–compressor system under closed Brayton cycle conditions is developed in Simulink, incorporating surge boundaries derived from performance maps. Control parameters are tuned using a weighted multi-objective fitness function that integrates overshoot, rise time, and the integral of absolute error. Simulation results demonstrate that the proposed control scheme markedly enhances system responsiveness—achieving approximately a 70% improvement in rotational speed regulation—and effectively maintains the operating point outside the surge region. The proposed framework provides a practical and robust approach for improving the dynamic stability and reliability of closed Brayton cycle power generation systems.