基于分布式优化的协同干扰任务分配研究

为保证目标区域干扰覆盖和最小能量消耗的优化目标，建立协同干扰任务分配模型。在分布式协同优化框架下，将集中式任务分配问题，转换为各个虚任务区内小规模的分布式优化问题，采用分解-协调优化模式和启发式遗传算法相结合的方法，实现对各个子区域优化问题的二次迭代求解。仿真结果表明，分布式协同优化方法能够有效降低协同干扰任务分配问题的求解规模，避免“维数灾”，具有可行性。

Research on Collaborative Jamming Task Assignment Based on Distributed Optimization

To the objectives of guaranteeing covering quality of jamming region and using the least energy consumption, a task assignment model of collaborative jamming is proposed. A centralized task assignment optimization decision is decomposed into the decentralized optimization of several single virtual task regions under the framework of Distributed Collaborative Optimization(DCO), and a method of optimization mode based on decomposition-coordination and heuristic genetic algorithm is implemented to the solution for the decentralized optimization. Simulation results show that the DCO-based method can significantly reduce the size of task assignment optimization decision problems and avoid the problem of “curse of dimensionality”, and that it is a feasible method for collaborative jamming.