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Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization

机译:基于粒子群算法的小型卫星运载火箭的多学科设计

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摘要

In the present paper, particle swarm optimization, a relatively new population based optimization technique, is applied to optimize the multidisciplinary design of a solid propellant launch vehicle. Propulsion, structure, aerodynamic (geometry) and three-degree of freedom trajectory simulation disciplines are used in an appropriate combination and minimum launch weight is considered as an objective function. In order to reduce the high computational cost and improve the performance of particle swarm optimization, an enhancement technique called fitness inheritance is proposed. Firstly, the conducted experiments over a set of benchmark functions demonstrate that the proposed method can preserve the quality of solutions while decreasing the computational cost considerably. Then, a comparison of the proposed algorithm against the original version of particle swarm optimization, sequential quadratic programming, and method of centers carried out over multidisciplinary design optimization of the design problem. The obtained results show a very good performance of the enhancement technique to find the global optimum with considerable decrease in number of function evaluations.
机译:在本文中,粒子群优化是一种相对较新的基于种群的优化技术,被用于优化固体推进剂运载火箭的多学科设计。推进,结构,空气动力学(几何)和三自由度轨迹模拟学科以适当的组合使用,并且最小发射重量被视为目标函数。为了减少高计算量并提高粒子群优化的性能,提出了一种称为适应度继承的增强技术。首先,在一组基准函数上进行的实验表明,该方法可以在保持解决方案质量的同时,大大降低计算成本。然后,针对设计问题的多学科设计优化,将提出的算法与原始版本的粒子群优化,顺序二次规划以及中心方法进行了比较。所获得的结果表明,增强功能的性能非常好,可以找到全局最优值,而功能评估的数量却大大减少。

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