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针对基本粒子群算法(Particle Swarm Optimization,PSO)易局部收敛的缺陷,设计一种根据种群多样性测度动态调整惯性权重的改进粒子群算法,通过仿真测试函数与基本粒子群算法、自适应粒子群算法(Adaptive Particle Swarm Optimization,APSO)、带收缩因子的粒子群算法(Contractive Particle Swarm Optimization,CPSO)进行比较,结果表明本文改进的PSO算法在提高算法的综合搜索能力方面具有优越性。将改进的PSO算法运用到岸基导弹对海上舰艇攻击火力分配中,构建了火力分配模型,并进行了仿真实验,仿真结果验证了模型及算法的有效性。
Aiming at the defect of easy local convergence of Particle Swarm Optimization (PSO), an improved Particle Swarm Optimization (PSO) algorithm is proposed to dynamically adjust the inertia weight according to the measure of population diversity. Through the simulation test function and the PSO algorithm, Adaptive Particle Swarm Optimization (APSO) and Contractive Particle Swarm Optimization (CPSO) are compared. The results show that the improved PSO algorithm in this paper is superior in improving the comprehensive search ability of the algorithm. The improved PSO algorithm is applied to the assault firepower assignment of shore-based missiles to the marine warship. The firepower distribution model is constructed and the simulation experiment is carried out. The simulation results verify the effectiveness of the model and the algorithm.