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摘要: 提出了一种基于遗传算法的纵断面优化方法, 这种方法可以在一个可行域中自动搜索一个最优或较优解。其基本思想是首先根据纵断面初始解建立一个可行域, 通过编码建立染色体与实际设计变量之间的一一对应关系, 然后对可行域中的可能解用一个评价函数(适应度)进行度量, 利用遗传算法在可行域中选择最优解。实践表明: 该方法具有全局解空间搜索能力, 从而实现了全局寻优的目的, 对道路优化设计是有效的, 可行的。Abstract: This paper put forward a new optimization method for highway profile with genetic algorithm, which could be used to search the optimal solution in a feasible zone. It built a feasible zone according to the vertical section initial solution, and use genetic algorithms to choose the optimal solution in the feasible zone by developing chromosome concerns with the real one after another correspondence designed between the variables, to evaluae the probably solution in the feasible zone with an evaluation function(degree of suiting). The application shows that this method is capable of searching the entire solution space in order to find the global optimum; it is effective and feasible to road optimization design.
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Key words:
- road engineering /
- genetic algorithms /
- highway /
- profile /
- optimization design
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表 1 采用遗传算法得到的设计结果
Table 1. Design result by genetic algorithms
序号 里程/m 高程/m 竖曲线半径/m 坡长/m 坡度/% 坡差 切线长/m 外距/m 直坡段长度/m 起点 0 382.34 0 0 0.00 0.000 0.00 0.00 0.00 1 180 385.38 3650 180 1.69 0.027 49.94 0.34 130.06 2 440 396.89 4120 260 4.42 -0.056 114.87 -1.60 95.18 3 670 394.25 3000 230 -1.15 0.044 66.10 0.73 49.03 4 890 401.41 4030 220 3.25 -0.055 109.83 -1.50 44.07 5 1120 396.37 3000 230 -2.20 0.035 52.79 0.46 67.37 6 1370 399.68 4500 250 1.32 -0.034 77.33 -0.66 119.88 7 1640 393.98 3000 270 -2.12 0.042 62.68 0.65 129.99 8 1960 400.59 4500 320 2.06 -0.043 96.78 -1.04 160.54 9 2390 390.99 3000 430 -2.24 0.039 58.91 0.58 274.30 10 3170 404.20 4500 780 1.69 -0.040 89.61 -0.89 631.47 11 3550 395.50 12500 380 -2.29 0.008 49.99 0.10 240.39 12 3860 390.88 3000 310 -1.49 0.040 60.18 0.60 199.83 13 4240 400.46 4500 380 2.52 -0.054 121.55 -1.64 198.28 14 4550 391.54 3000 310 -2.88 0.038 56.36 0.53 132.09 15 4910 394.69 4500 360 0.87 -0.026 59.57 -0.39 244.07 16 5300 387.79 3190 390 -1.78 0.031 50.02 0.39 280.41 17 5610 392.03 6700 310 1.36 0.015 49.97 -0.19 210.01 18 5940 391.61 9760 330 -0.13 0.010 49.98 0.13 230.05 19 6270 394.58 5540 330 0.89 0.018 50.02 0.23 230.00 20 6690 405.93 3290 420 2.70 -0.070 114.83 -2.00 255.15 终点 6920 396.10 0 230 -4.28 0.000 0.00 0 115.17 优化系数 0.963 最大纵坡/% 最小纵坡/% 最小半径/m 竖曲线最小长度/m 最小直坡段长度/m 每公里变坡点数 4.42 0.13 3000 99.88 44.07 3.03 表 2 人工设计结果
Table 2. Manual design result
序号 里程/m 高程/m 竖曲线半径/m 坡长/m 坡度/% 坡差 切线长/m 外距/m 直坡段长度/m 起点 0 382.34 0 0 0.00 0.000 0.00 0.00 0.00 1 460 396.60 5000 460 3.09 -0.028 69.36 -0.48 390.64 2 890 398.00 15000 430 0.32 -0.007 54.42 -0.10 306.22 3 1640 395.00 6000 750 -0.40 0.022 64.50 0.35 631.08 4 1960 400.60 6000 320 1.75 -0.040 119.48 -1.19 136.02 5 2390 391.00 6000 430 -2.24 0.038 113.73 1.08 196.79 6 3160 403.00 6000 770 1.55 -0.032 94.84 -0.75 561.43 7 3840 392.10 6000 680 -1.61 0.027 81.25 0.55 503.91 8 4410 398.40 6000 570 1.10 -0.022 64.84 -0.35 423.91 9 5300 389.00 8000 890 -1.06 0.016 63.99 0.26 761.17 10 6220 394.00 8000 920 0.54 0.016 63.98 0.26 792.04 11 6710 404.50 4500 490 2.14 -0.061 138.23 -2.12 287.80 终点 6920 396.10 0 210 -4.01 0.000 0.00 0.00 71.77 优化系数 1.146485 最大纵坡/% 最小纵坡/% 最小半径/m 竖曲线最小长度/m 最小直坡段长度/m 每公里变坡点数 4 0.32 4500 108.84 71.77 1.73 -
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