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论文研究-基于遗传算法的水电站优化调度新方法.pdf

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论文研究-基于遗传算法的水电站优化调度新方法.pdf, 提出求解水电站优化调度问题的新方法——遗传算法，它不同于经典优化算法的特点是，从多个初值点开始寻优，沿多路径搜索实现全局或准全局最优，计算过程中不需要存储状态或决策变量离散点，大大减少计算机内存，不必求导运算，编程简单，是一种有效的自适应随机搜索算法.
7 67 ( ev aluat ion function) select ior V) LL (V) (7) (V) (i=1,2,,POP) (1)(5) 8),(V)ms O(V)min POP (1)(5) (V)=F-M·>W F W ((2)-(5)) (M at ing pool P。POP Grossover) POP POS POS POS+ 1 (n=32) P=(100011000101101001111000011001) POS Pk=(1110l110101110001000111110111l POS Pos= 9 P1=(1000110001011100010001111011) Pk=(1110l1l110110100111000011001 P。POP (m ut atio n) P POP [1,2,,n] Bit. Bit Bit 0 Pk=(1110110110110100111100001100) Bit Bit= 2 68 19977 Pk=(10101110110110100111100001100) Bit 1)G=0(G P.Pn,P,∈ POP 4) G= G (G+ (G) (G) G 1200 1155 330 23783/ 30 146 151.4 0 393472bv 1098 12bytes 表3G(代数)变化时遗传算法计算结果 100 151.4 150 152.3 500 7 69 表1遗传算法计算结果 (gene= 100) 2460 1160.0 1481.9 147.7 188.2 6789 l193.8 2148.1 147.9 273.3 3250 1198.0 2378.0 141.6 266.1 802.3 1940 1200.0 1940.0 158.6 264.6 1710 1200.0 1826.1 160.2 251.6 1040 1196.6 972.0 178.5 149.2 12 9 1198.6 697.1 182.8 109.6 460 1197.2 661.4 179.5 102.1 12345 425 l191.2 661.5 98.4 409 l184.2 743.3 162.6 103.9 443 l172.4 148.7 102.6 44 1158.1 1518.0 126.5 165.2 E=151.4 Pc=0.2,Ps=0.2,Pm=0.1 表2动态规划计算成果 2460 l160.0 1338.7 153.0 176.2 2290 l198.0 2221.6 148.6 283.9 250 2378.0 142.5 268.9 872.0 1940 1200.0 1940.0 158.6 1710 1200.0 1710.0 165.1 242.8 1040 1200.0 1040.0 179.8 160.8 1200.0 649.0 185.5 103.5 460 1200.0 652.0 182.6 102.4 425 1194.4 650.9 176.5 2345 409 1187.6 702.4 168.0 101.5 1178.6 741.3 157.3 100.3 544 1167.4 825.1 144.4 102.4 E=146.4 19977 2 200 400 6000 200 400 600 7 C 8C Barton DK. R adar System Analysis, Artech, 1979. 2 Barton dK. Wardhr. hand book of Radar M easure. 19 69 3 4 Zhu Lix ing, Hickernell FJ. An Hongzhi. A goo dness -of fit test for linear it y of stoch as tic reg ress ion model, Chinese Annals of Math, 1997 5 An Hongzhi, Gu Lan. On the selectio n of regression variables. Acta Math. A ppl, Sinica 1985,(2), 27 6 1993 69) Goldberg d g. Genetic algorithms in search optimization a nd machine learning. a ddison Wesley, 1989 2 Grefenstette JJ Optimizatio n of co nt rol parameters for genetic alg or it hm s. IEEE Trans on syst. man and cyber netics, Vol. SMC-16. No. 1, Jan / Feb, 1986 Goldberg D E, Deb K. A co mparative anal ysis of select ion schemes used in genetic algorithms. Proe of The Fourt h Int ernational Conf on G enet ic A lg or ithms, 1991 4 Walters G A, Lohbeck T.Op ptimal layo ut of tree net works using genetic alg orithms Eng. Opt., 1993 5 Yeh w. Reservo ir management and operations mo dels a state of the art review. Water resources res 1985,2l(12)

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