基于初始种群优化粒子群算法的通信抗干扰决策引擎

An Initial Population Optimization- based Particle Swarm Algorithm for Anti- Jamming Communication Decision Engine

针对抗干扰决策引擎对实时性能要求较高的问题,借鉴了一种基于初始种群优化的粒子群算法(IPO-PSO)。该算法通过把上一次决策的部分解作为当前初始解集的一部分,以此来优化粒子群算法的初始种群。仿真结果表明,该算法能够在不增加复杂度的情况下显著提高粒子群算法在缓变干扰环境下的收敛速度,具有较好的实时性能,更加符合通信抗干扰的应用场景。

Aiming at the high requirement of real-time performance for anti-jamming decision engine, an IPO-PSO （Initial Population Optimization Particle Swarm Algorithm）is referred. The algorithm takes part of the solution in the last decision as part of the initial solution set, so as to improve the initial population of the particle swarm algorithm. Simulation results show that this algorithm could obviously increase the convergence rate of the particle swarm optimization under the slowly-varying interference condition without the increase of complexity. So this algorithm enjoys better real-time performance and is more in line with the application scenarios of military anti-jamming communication.