基于改进QPSO算法的GEO卫星时频资源调度

Time-frequency Resource Scheduling Based on Improved Quantum-behaved Particle Swarm Optimization Algorithm for GEO Satellite

元启发式算法已广泛应用于GEO通信卫星时频资源调度问题,但常见的任务资源窗口更新方法和基于任务路径的蚁群算法存在计算复杂度过高的问题。针对该问题,提出了一种基于资源矩阵更新的调度策略,通过将任务资源匹配过程转化为矩阵的Hadamard乘积运算,有效降低了资源匹配过程的计算复杂度。在此基础上,设计了一种采用实数编码、最小位置值规则解码和初始解优化的改进量子粒子群算法,将连续型元启发式算法应用于任务序列的优化,实现了计算复杂度的进一步降低和优化解的提升。仿真实验结果表明,提出的改进算法在执行时间、优化质量和任务完成率等指标上均表现出较好的性能。

Meta-heuristic algorithms have been widely used in the GEO communication satellite time-frequency resource scheduling,but the computational complexity of common task resource window update method and task path-based ant colony algorithm is high.A scheduling strategy based on resource matrix update is proposed to address this problem,which effectively reduces the computational complexity of the resource matching process by transforming it into a Hadamard product operation of the matrix.On this basis,an improved quantum-behaved particle swarm algorithm with real number encoding,minimum location value rule decoding and initial solution optimization is designed,and a continuous type meta-heuristic algorithm is applied to the optimization of task sequences,which achieves further reduction of computational complexity and improvement of optimal solutions.The results of simulation experiments show that the proposed improved algorithm exhibits better performance in terms of execution time,optimization quality and task completion rate.