實數編碼利基遺傳演算法應用於河川不恆定流模式阻力參數率定之研究
Date Issued
2004
Date
2004
Author(s)
詹明修
DOI
zh-TW
Abstract
Based on imitation mechanics of “the natural selection and the survival of the fittest”, genetic algorithm was developed by John Holland in the 1960s. In this paper, a real-valued-coding niche genetic algorithm modifying the binary-coding niche genetic algorithm (Goldberg and Richardson, 1987) is developed for searching global minimum value of a multiple-peak function efficiently. To demonstrate the advantage of this developed algorithm, we compare present methods with real-valued-coding genetic algorithm, binary-coding niche genetic algorithm and binary-coding genetic algorithm. The results show that the real-valued-coding niche genetic algorithm developed in this thesis is the best among the four methods mentioned above.
The second purpose of this thesis is to automate the calibration process of CCCMMOC model (Lai, 1999) by incorporating a real-valued-coding niche genetic algorithm. We first calibrate the resistance coefficients of CCCMMOC and then verify them. The target river system is Tamsui River system which is divided into 17 reaches. Each reach corresponds to a resistance coefficient. The objective function is root-mean-square error of the difference between the measured and calculated water levels at all observation stations. The constraints are 1-D unsteady flow model (CCCMMOC), and the upper and lower limits of resistance coefficients.
The results are promising because the resistance coefficients calibrated by present algorithm with different initial population points converge to the same values. On the other hand, the simulated results of water levels are in good agreement with the observed whether in the calibration case or the verification case. Therefore, the real-valued-coding niche genetic algorithm developed in this thesis is a good automatic calibration method which can find the global optimum accurately and objectively.
Subjects
實數編碼利基遺傳演算法
遺傳演算法
real-valued-coding niche genetic algorithm
genetic algorithm
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-93-R91521314-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):66561a122ffa046d2f70df37f4b376cc