群智能算法在飞行器总体设计中的应用  

Swarm Intelligence Algorithm and Its Application to Vehicle Overall Design

作　　者：池元成[1] 陆小兵[1] 王彦静[1] 刘维玮[1] 郭大庆[1] 

机构地区：[1]中国运载火箭技术研究院研发中心,北京100076

出　　处：《导弹与航天运载技术》2015年第3期38-41,共4页Missiles and Space Vehicles

摘　　要：飞行器总体设计直接影响飞行器的战术技术指标,在其设计过程中往往要经过多次反复,因此需要优化手段获得较优的飞行器总体技术方案。群智能算法由于自身的特点受到广泛关注,应用前景广泛。本文选择常用的3种群智能算法,即粒子群优化算法、差分进化算法和蚁群算法对某飞行器总体方案进行优化设计,并对结果进行对比分析。Overall design directly impacts the tactic and technology indexes of a flight vehicle. Because designing scheme is often repeated in vehicle design process, optimization method is required to obtain optimal technical proposal of vehicle design. Swarm intelligence algorithm drowns extensive attention and has a promising prospect because of its characteristics. Three swarm intelligence methods, namely particle swarm optimization, differential evolution and ant colony algorithm, are used to optimize the design of a vehicle, and the results are compared in this paper.

关 键 词：群智能算法 飞行器总体设计 粒子群优化算法 差分进化 蚁群算法 

分 类 号：V421.1[航空宇航科学与技术—飞行器设计]