并行加工系统一类排序问题的最优解

讨论了将多个零件分派给并行加工系统加工的排序问题.假设同一零件分配给不同的设备,其加工时间不同,分析了使所有零件的总花费时间(加工时间与等待时间之和)最小的排序方法.首先建立了该类问题的数学模型,然后将其转化为指派问题,通过匈牙利算法可以得到最优解.所得算法的时间复杂性是多项式界的.最后给出了一个数值例子说明求解过程.     

高校排课中的遗传算法

采用了鲁棒性较好的遗传算法,对院校的必修课给出了染色体编码和适应度函数,并采用了自适应的杂交和变异概率,程序运行结果良好。     

求解TSP问题的遗传算法

无人侦察机的巡航问题,如果不考虑其它约束条件,实际上是一个 TSP 问题。目前还没有求解 TSP 问题的比较有效的实时算法,我们首先利用改良圈算法求得一个较好的初始种群,再应用遗传算法就可以实时地求得一个较满意的解。     

牛顿迭代收敛的加速

基于Newton迭代法单根的二阶收敛性和重根的线性收敛性,提出了加速牛顿迭代收敛的思想。利用反函数的性质,取Taylor展开式的前三项进行迭代;并利用差商代替导数的方法,构造出更高收敛阶的迭代公式。大量的数值实验结果表明,本文算法理论上的推导是完全可行的,且有效地提高了迭代公式的收敛速度。     

卫星数传调度问题的速度可控粒子群优化算法

针对卫星数传调度这一类复杂的约束组合优化问题,提出了一种速度方向和尺度可控的粒子群优化求解算法。为克服粒子群算法的早熟问题,该算法引入了速度更新的方向控制规则和尺度控制规则用于增加群体的多样性,并根据两种控制规则,提出了种群粒子的速度迭代策略,实现了算法运行过程中粒子间吸引和排斥过程的动态调整,同时保证了算法的收敛速度。设计了卫星数传调度问题的编码和解码方式,并证明了该编码方式下最优可行解的存在性。通过仿真实例验证了算法的有效性,并分析了算法控制参数对优化结果的影响。     

反演航天器在轨瞬态外热流的导热反问题

获得航天器在轨飞行过程中的外热流数据对于研究热控涂层在轨退化规律、各种空间因素对热控产品的影响以及航天器姿轨控发动机羽流热效应都有非常重要的意义,然而直接测量热流存在很多困难,因此可以通过求解导热反问题得到满足一定精度的结果.首先,通过研究利用航天器设备在轨遥测温度值反演出航天器在轨瞬态外热流的导热反问题方法,建立了反演航天器在轨瞬态外热流的数学模型,采用共轭梯度法求解导热反问题并从物理概念角度改进了共轭梯度法的迭代过程以增加其抗不适定性;然后构造了两组能够代表目前大多数地球轨道航天器以及深空探测航天器在轨吸收外热流变化的数值试验对共轭梯度法的反演效果进行了检验.除阶跃变化位置以外反演值与真实值的最大相对偏差为2.9%,反演效果非常好;对于阶跃变化位置的吸收外热流在对反演结果进行分析处理后也能够得到较好的反演结果.      

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.     

Thick galactic cosmic radiation shielding using atmospheric data

NASA is concerned with protecting astronauts from the effects of galactic cosmic radiation and has expended substantial effort in the development of computer models to predict the shielding obtained from various materials. However, these models were only developed for shields up to about 120 g/cm² in mass thickness and have predicted that shields of this mass thickness are insufficient to provide adequate protection for extended deep space flights. Consequently, effort is underway to extend the range of these models to thicker shields and experimental data is required to help confirm the resulting code. In this paper empirically obtained effective dose measurements from aircraft flights in the atmosphere are used to obtain the radiation shielding function of the Earth's atmosphere, a very thick, i.e. high mass, shield. Obtaining this result required solving an inverse problem and the method for solving it is presented. The results are shown to be in agreement with current code in the ranges where they overlap. These results are then checked and used to predict the radiation dosage under thick shields such as planetary regolith and the atmosphere of Venus.