基于离散度多目标粒子群重频组优化算法

Optimized Algorithm of PRF Set Based on Dispersion Improved MOPSO

针对重频组优化需考虑的解模糊、抗盲区、抗虚影等因素,提出了一种基于离散度的多目标粒子群重频组优化算法;针对雷达重频组优化问题,以最小化虚影产生率、最小化盲区、最小虚影为目标综合设计重频组,提出基于改进MOPSO重频组优化算法,求解出雷达重频组的最优pareto前沿,并引入灰色关联度用于折中解的选择,最后通过仿真验证了改进MOPSO重频组优化算法在寻优能力、收敛性、减少盲区能力、抗虚影能力和解模糊能力方面均优于传统MOPSO算法。

A multi-objective particle swarm optimization algorithm(MOPSO)based on dispersion was proposed to solve the ambiguity,anti-blind area,anti-shadow and other factors that need to be considered in the optimization of pulse repetition frequency(PRF)set.Aiming at the optimization problem of PRF set,a comprehensive design of PRF set is made with the objective of minimizing the ghost generation rate,minimizing the blind area and minimizing the ghost as the goal.An improved MOPSO PRF set optimization algorithm was proposed to solve the optimal Pareto front of PRF set,and the grey relational degree was introduced to select the compromise solution.Finally,the simulation experiment proves that the improved MOPSO PRF set optimization algorithm is superior to the traditional MOPSO algorithm in terms of optimization ability,convergence,blind area reduction ability,anti-shadow ability and ambiguity resolution ability.