关键词: 甚高频数据交换系统, 资源分配, 遗传算法, 粒子群优化, 改进遗传-粒子群优化算法

Abstract:

In the resource allocation of very high frequency data exchange system （VDES）, traditional allocation strategies face challenges, including insufficient dynamic adaptation capability and low communication efficiency. This limits the system’s total channel capacity and reduces the service success rate. This paper establishes a satellite-ship communication model. Constraints include maximum satellite coverage time and ship task demand time. It introduces adaptive parameter adjustment and elite retention strategies. An improved genetic algorithm-particle swarm optimization （IGA-PSO） hybrid algorithm is proposed. Experiments are conducted on the CEC2022 function test set and in VDES resource allocation scenarios. Experimental results show that the proposed IGA-PSO algorithm reduces the average number of iterations by 83.9% and 82.3% compared to genetic algorithm （GA） and sparrow search algorithm （SSA）, respectively. It also improves the mean total channel capacity by 16.71% and 12.02% over GA and SSA, respectively. The mean service success rate increases by 40.2% and 30.43% over GA and SSA, respectively. IGA-PSO’s mean total channel capacity and mean service success rate are generally the best or similar to particle swarm optimization （PSO） and whale optimization algorithm （WOA）. Furthermore, the average number of iterations is reduced by 78.6% and 71.8% compared to PSO and WOA, respectively.

Key words: very high frequency data exchange system (VDES), resource allocation, genetic algorithm (GA), particle swarm optimization (PSO), improved genetic algorithm-particle swarm optimization (IGA-PSO)