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针对激励限制下的共形阵功率方向图综合问题,提出一种改进的多目标粒子群优化(IMOPSO)算法。将共形阵列在激励限制条件下的综合命题,转化为激励优化和功率方向图赋形的多目标优化命题。IMOPSO算法通过引入多子群寻优、粒子聚焦距离优选、非支配解集修剪以及新生粒子微扰复制等机制,显著提高了传统多目标粒子群优化(MOP-SO)算法所构建Pareto解集的优越性和散布性。IMOPSO算法成功用于12元微带柱面共形阵非赤道面的方向图综合,获得了不同约束条件下最优余割平方波束方向图综合结果集合,综合过程考虑了各阵元的互耦作用,为规划共形相控阵的激励限制提供了极有价值的参考。
Aiming at the problem of conformal array power pattern synthesis under incentive limitation, an improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed. The conception of conformal array under incentive constraints is transformed into multi-objective optimization proposition for excitation optimization and power patterning. The IMOPSO algorithm significantly improves the Pareto solution set constructed by the traditional multi-objective particle swarm optimization (MOP-SO) algorithm by introducing mechanisms such as multi-subgroup optimization, optimal particle focusing distance, non-dominated solution set pruning, and newborn particle perturbation replication Superiority and distribution. The IMOPSO algorithm is successfully applied to the non-equatorial 12-D microstrip cylindrical shape of the non-equatorial surface synthesis, obtained under different constraints, the optimal result of the cutting-edge square beam pattern synthesis results set, the integrated process to consider the mutual coupling of each element The role of planning conformal phased array excitation provides a very valuable reference.