改进遗传算法在初始波长定标中的应用  

The Application of an Improved Genetic Algorithm to the Initial Wavelength Calibration

作　　者：薛续磊[1] 叶中付[1] 

机构地区：[1]中国科学技术大学信号统计处理研究室,安徽合肥230027

出　　处：《天文研究与技术》2009年第3期181-190,共10页Astronomical Research & Technology

基　　金：国家"九五"重大科学工程项目(98BJG001)资助

摘　　要：在LAMOST和SDSS二维光谱数据处理的初始波长定标操作中,通常给定色散曲线的一组初始拟合系数,然后在其附近一定空间内进行搜索,以期找到一组最优的系数。这实际上是一个全局寻优的问题。LAMOST和SDSS目前使用的是枚举式搜索方式,但是由于缺乏全局最优解的先验知识,需要大量的时间遍历整个解空间才能得到全局最优解。遗传算法由于使用了启发式搜索方式,是一种高效的全局寻优算法。在标准遗传算法的框架上,通过使用有效的编码方式、适应度函数以及选择、交叉、变异等遗传算子,构造了一种能够用于初始波长定标的快速收敛的改进遗传算法。通过Shaffer’s F6函数的测试,该改进遗传算法具有良好的全局收敛性。将该改进遗传算法引入到LAMOST初始波长定标的寻优操作中,实验表明该算法能够取得较好的效果。In the initial wavelength calibration for the processing of two-dimensional spectral data from the LAMOST and SDSS, searches are usually performed to find the optimum coefficients to fit the dispersion relation. These are achieved by giving a set of initial coefficient values, and then searching the optimum solution in the proximity of the multidimensional space of the relevant coefficients. The calibration is actually a global optimization problem. The method now used for the LAMOST and SDSS is the enumerative search. Since such a method lacks the prior information of optimum solution, the search checks the entire space of coefficients and spends a large amount of time to pin down the global optimum solution. As a genetic algorithm uses the heuristic search, it is efficient in global optimization and is being increasingly applied in different research areas. Under the framework of a standard genetic algorithm, which is very simple, there are usually some practical disadvantages of premature convergence--the algorithm does not converge to the global optimum solution but to a local optimum solution. To overcome the problems, we design an improved genetic algorithm with suitable coding, fitness functions, and genetic operators. We apply the algorithm to the initial wavelength calibration. Specifically, the float -point coding is used to encode the individual genes, which is more exact for the solution than the binary coding. The genetic operators consist of fitness - proportional reproduction, elitist selection, heuristic crossover operator, mutation operator, and cataclysm operator, all of which are originally introduced for the research of biological evolution. We use Shaffer＇ s F6 function to test the convergence of the improved genetic algorithm by letting the algorithm search the global maximum of the function. It is shown that the improved algorithm can converge to the global optimum solution via enough searches. Subsequently, the improved algorithm is applied to the simulated initial wavelength calibratio

关 键 词：遗传算法 适应度 优化 初始波长定标 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]