基于改进QPSO算法的阵列天线方向图综合

摘要

In view of some short comings ,such as the premature convergence and bad local optimal searching capability in traditional intelligence methods for pattern synthesis ,a novel algorithm is proposed based on quantum particle swarm optimization (QPSO ) with probability amplitude coding of quantum bits ,which is designed by use of stagnation detection and selective variation in particles and is applied in the pattern synthesis of array anttenas .The simulation results show its high performance in the pattern synthesis of array anttenas with multi-null and low sidelobe restrictions ,and in addition ,the algorithm proposed is superior to neigh-borhood particle swarm optimization (NPSO )and immune clonal selection algorithm (ICSA )in optimization accuracy and operation speed ,and it has very good generalization capability .%针对传统智能方法在方向图综合中易于早熟和局部寻优能力不足等缺陷，在基于量子位概率幅编码的量子粒子群优化算法（QPSO ）的基础上，设计一种进行收敛停滞检测，并对粒子选择性变异的新量子粒子群算法，然后将其应用于阵列天线方向图综合。仿真结果表明，在多零点和低旁瓣约束情况下新算法均可以取得良好的优化效果，而且该算法相对于近邻粒子群算法（NPSO ）和免疫克隆选择算法（ICSA ）来说，在方向图综合中精度更高，速度更快，具有很好的推广能力。