降低固体推进剂特征信号的改进粒子群算法  

Improved particle swarm algorithm to lower characteristic signal of solid propellant

作　　者：赵玖玲 张文海 ZHAO Jiuling;ZHANG Wenhai(Department of Power Engineering, Rocket Force University of Engineering, Xi’an 710025, China)

机构地区：[1]火箭军工程大学动力工程系,西安710025

出　　处：《计算机工程与应用》2017年第19期136-141,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.51276192)

摘　　要：固体推进剂燃烧产生的特征信号越来越成为制约导弹武器隐身特性和制导精度发展的重要因素,为解决传统实验法降低特征信号而进行配方设计周期长的问题,采用粒子群算法寻找固体推进剂配方最优设计方案以达到降低特征信号的目的,同时综合运用拒绝法、罚函数法以及种群多样性保持策略对标准粒子群算法进行适当改进,解决了非线性约束问题,克服了算法容易陷入局部最优的缺陷,提高了全局搜索能力。建立配方优化数学模型并进行仿真,结果表明采用改进粒子群算法来降低固体推进剂特征信号,优于标准粒子群算法、改进遗传算法等智能算法,并能缩短配方设计周期。Characteristic signal of solid propellant combustion more and more becomes an important factor to restrictmissile stealth characteristics and guidance precision development.In order to solve the problem of long formulationdesign cycle to reduce the characteristic signal,which is caused by traditional experimental method,Particle SwarmOptimization Algorithm(PSOA)is studied to find the optimal design scheme of solid propellant formulations to reducecharacteristic signal.In the process,the rejection method,the penalty function method and the strategy of keeping populationdiversity are used to improve standard PSOA properly.It solves the nonlinear constraint problem,overcomes the defectsof algorithm to fall into local optimum easily and improves global search ability.Establishment of the formulation optimizationmodel and simulation results show that in the aspect of reducing characteristic signal,improved PSOA is superiorto some other intelligent algorithms,such as,improved genetic algorithm,standard particle swarm optimization and soon,and it can also shorten the formulation design cycle.

关 键 词：粒子群算法 拒绝法 罚函数法 种群多样性保持策略 配方优化数学模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]