基于量子粒子群优化的多波束卫星联合资源分配算法

Joint resource allocation algorithm for multi-beam satellite based on quantum-behaved particle swarm optimization

当使用元启发式算法求解多波束卫星联合资源分配问题时,时延约束和容量约束会导致计算复杂度增大,且算法难以收敛。对此,通过在目标函数中引入惩罚机制,在无效解的目标函数值加入了惩罚值,使得算法的优化解自适应地满足这两个约束。在此基础上,提出了基于量子粒子群优化的联合资源分配算法。仿真结果表明,惩罚策略的引入解决了应用元启发式算法时,难以处理时延约束和容量约束的问题,而带有惩罚机制的量子粒子群算法在分配公平性指数、总系统容量上均优于已有联合分配算法。

When the meta-heuristic algorithm solves the joint resource allocation problem of multi-beam satellites, the computational complexity increases and the algorithm is difficult to converge due to the time delay constraint and capacity constraint.This paper introduced a penalty mechanism in the objective function, and added a penalty value to the objective function of the invalid solution, so that the optimized solution adaptively satisfied these two constraints.Based on this, this paper proposed a joint resource allocation algorithm based on quantum-behaved particle swarm optimization.Simulation results show that the introduction of the penalty strategy solves the problem of difficulty in handling the delay constraint and capacity constraint when applying the meta-heuristic algorithm.The quantum-behaved particle swarm optimization algorithm with penalty mechanism outperforms the existing joint allocation algorithm in terms of allocation fairness index and total system capacity.