CAJ | 학술논문

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。
침대전통지능방법재방향도종합중역우조숙화국부심우능력불족등결함，재기우양자위개솔폭편마적양자입자군우화산법（QPSO ）적기출상，설계일충진행수렴정체검측，병대입자선택성변이적신양자입자군산법，연후장기응용우진렬천선방향도종합。방진결과표명，재다영점화저방판약속정황하신산법균가이취득량호적우화효과，이차해산법상대우근린입자군산법（NPSO ）화면역극륭선택산법（ICSA ）래설，재방향도종합중정도경고，속도경쾌，구유흔호적추엄능력。
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 .