【无功优化】基于粒子群算法实现潮流无功优化附matlab代码

简介: 【无功优化】基于粒子群算法实现潮流无功优化附matlab代码

✅作者简介:热爱科研的Matlab仿真开发者,修心和技术同步精进,matlab项目合作可私信。

🍎个人主页:Matlab科研工作室

🍊个人信条:格物致知。

更多Matlab仿真内容点击👇

智能优化算法       神经网络预测       雷达通信      无线传感器        电力系统

信号处理              图像处理               路径规划       元胞自动机        无人机

⛄ 内容介绍

电力系统无功优化是保证系统安全,经济运行的有效手段,是提高电力系统电压质量的重要措施之一。无功优化的目的在于确定系统中无功设备的合理配置,以保证电网在满足一定的安全约束条件下,使系统的技术经济性能指标最好,即无功补偿设备的安装投资及电网的运行费用最小。 电力系统无功优化是一个多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,其优化过程比较复杂。本文采用粒子群优化算法求解无功优化问题,给出了运用粒子群优化算法的相应求解步骡,并对部分参数的取值进行了讨论和调整。计算表明,该算法能够同时处理问题中的连续变量和离散变量,能够较好地协调全局搜索和局部搜索,并具有并行计算的特性以及较强的鲁棒性,可以取得令人满意的结果。 本文对IEEE30,IEEE57节点试验系统和石家庄高邑县电网进行了无功优化计算,取得了令人满意的结果,表明用粒子群优化算法求解该问题是可行的、更有效的

⛄ 部分代码

function [baseMVA, bus, gen, branch, areas, gencost] = case_ieee30

% CASE_IEEE30    Power flow data for IEEE 30 bus test case.

%   Please see 'help caseformat' for details on the case file format.

%   This data was converted from IEEE Common Data Format

%   (ieee30cdf.txt) on 20-Sep-2004 by cdf2matp, rev. 1.11

%   See end of file for warnings generated during conversion.

%

%   Converted from IEEE CDF file from:

%       http://www.ee.washington.edu/research/pstca/

%

%  08/20/93 UW ARCHIVE           100.0  1961 W IEEE 30 Bus Test Case


%   MATPOWER

%   $Id: case_ieee30.m,v 1.3 2007/09/17 16:07:48 ray Exp $


%%-----  Power Flow Data  -----%%

%% system MVA base

baseMVA = 100;


%% bus data

% bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin

bus = [

1 3 0 0 0 0 1 1.06 0 132 1 1.06 0.94;

2 2 21.7 12.7 0 0 1 1.043 -5.48 132 1 1.06 0.94;

3 1 2.4 1.2 0 0 1 1.021 -7.96 132 1 1.06 0.94;

4 1 7.6 1.6 0 0 1 1.012 -9.62 132 1 1.06 0.94;

5 2 94.2 19 0 0 1 1.01 -14.37 132 1 1.06 0.94;

6 1 0 0 0 0 1 1.01 -11.34 132 1 1.06 0.94;

7 1 22.8 10.9 0 0 1 1.002 -13.12 132 1 1.06 0.94;

8 2 30 30 0 0 1 1.01 -12.1 132 1 1.06 0.94;

9 1 0 0 0 0 1 1.051 -14.38 1 1 1.06 0.94;

10 1 5.8 2 0 19 1 1.045 -15.97 33 1 1.06 0.94;

11 2 0 0 0 0 1 1.082 -14.39 11 1 1.06 0.94;

12 1 11.2 7.5 0 0 1 1.057 -15.24 33 1 1.06 0.94;

13 2 0 0 0 0 1 1.071 -15.24 11 1 1.06 0.94;

14 1 6.2 1.6 0 0 1 1.042 -16.13 33 1 1.06 0.94;

15 1 8.2 2.5 0 0 1 1.038 -16.22 33 1 1.06 0.94;

16 1 3.5 1.8 0 0 1 1.045 -15.83 33 1 1.06 0.94;

17 1 9 5.8 0 0 1 1.04 -16.14 33 1 1.06 0.94;

18 1 3.2 0.9 0 0 1 1.028 -16.82 33 1 1.06 0.94;

19 1 9.5 3.4 0 0 1 1.026 -17 33 1 1.06 0.94;

20 1 2.2 0.7 0 0 1 1.03 -16.8 33 1 1.06 0.94;

21 1 17.5 11.2 0 0 1 1.033 -16.42 33 1 1.06 0.94;

22 1 0 0 0 0 1 1.033 -16.41 33 1 1.06 0.94;

23 1 3.2 1.6 0 0 1 1.027 -16.61 33 1 1.06 0.94;

24 1 8.7 6.7 0 4.3 1 1.021 -16.78 33 1 1.06 0.94;

25 1 0 0 0 0 1 1.017 -16.35 33 1 1.06 0.94;

26 1 3.5 2.3 0 0 1 1 -16.77 33 1 1.06 0.94;

27 1 0 0 0 0 1 1.023 -15.82 33 1 1.06 0.94;

28 1 0 0 0 0 1 1.007 -11.97 132 1 1.06 0.94;

29 1 2.4 0.9 0 0 1 1.003 -17.06 33 1 1.06 0.94;

30 1 10.6 1.9 0 0 1 0.992 -17.94 33 1 1.06 0.94;

];


%% generator data

% bus Pg Qg Qmax Qmin Vg mBase status Pmax Pmin

gen = [

1 260.2 -16.1 10 0 1.06 100 1 360.2 0;

2 40 50 50 -40 1.045 100 1 140 0;

5 0 37 40 -40 1.01 100 1 100 0;

8 0 37.3 40 -10 1.01 100 1 100 0;

11 0 16.2 24 -6 1.082 100 1 100 0;

13 0 10.6 24 -6 1.071 100 1 100 0;

];

x=[1.06

%% branch data

% fbus tbus r x b rateA rateB rateC ratio angle status

branch = [

1 2 0.0192 0.0575 0.0528 9900 0 0 0 0 1;

1 3 0.0452 0.1652 0.0408 9900 0 0 0 0 1;

2 4 0.057 0.1737 0.0368 9900 0 0 0 0 1;

3 4 0.0132 0.0379 0.0084 9900 0 0 0 0 1;

2 5 0.0472 0.1983 0.0418 9900 0 0 0 0 1;

2 6 0.0581 0.1763 0.0374 9900 0 0 0 0 1;

4 6 0.0119 0.0414 0.009 9900 0 0 0 0 1;

5 7 0.046 0.116 0.0204 9900 0 0 0 0 1;

6 7 0.0267 0.082 0.017 9900 0 0 0 0 1;

6 8 0.012 0.042 0.009 9900 0 0 0 0 1;

6 9 0 0.208 0 9900 0 0 0.978 0 1;

6 10 0 0.556 0 9900 0 0 0.969 0 1;

9 11 0 0.208 0 9900 0 0 0 0 1;

9 10 0 0.11 0 9900 0 0 0 0 1;

4 12 0 0.256 0 9900 0 0 0.932 0 1;

12 13 0 0.14 0 9900 0 0 0 0 1;

12 14 0.1231 0.2559 0 9900 0 0 0 0 1;

12 15 0.0662 0.1304 0 9900 0 0 0 0 1;

12 16 0.0945 0.1987 0 9900 0 0 0 0 1;

14 15 0.221 0.1997 0 9900 0 0 0 0 1;

16 17 0.0524 0.1923 0 9900 0 0 0 0 1;

15 18 0.1073 0.2185 0 9900 0 0 0 0 1;

18 19 0.0639 0.1292 0 9900 0 0 0 0 1;

19 20 0.034 0.068 0 9900 0 0 0 0 1;

10 20 0.0936 0.209 0 9900 0 0 0 0 1;

10 17 0.0324 0.0845 0 9900 0 0 0 0 1;

10 21 0.0348 0.0749 0 9900 0 0 0 0 1;

10 22 0.0727 0.1499 0 9900 0 0 0 0 1;

21 22 0.0116 0.0236 0 9900 0 0 0 0 1;

15 23 0.1 0.202 0 9900 0 0 0 0 1;

22 24 0.115 0.179 0 9900 0 0 0 0 1;

23 24 0.132 0.27 0 9900 0 0 0 0 1;

24 25 0.1885 0.3292 0 9900 0 0 0 0 1;

25 26 0.2544 0.38 0 9900 0 0 0 0 1;

25 27 0.1093 0.2087 0 9900 0 0 0 0 1;

28 27 0 0.396 0 9900 0 0 0.968 0 1;

27 29 0.2198 0.4153 0 9900 0 0 0 0 1;

27 30 0.3202 0.6027 0 9900 0 0 0 0 1;

29 30 0.2399 0.4533 0 9900 0 0 0 0 1;

8 28 0.0636 0.2 0.0428 9900 0 0 0 0 1;

6 28 0.0169 0.0599 0.013 9900 0 0 0 0 1;

];


%%-----  OPF Data  -----%%

%% area data

areas = [

1 1;

];


%% generator cost data

% 1 startup shutdown n x1 y1 ... xn yn

% 2 startup shutdown n c(n-1) ... c0

gencost = [

2 0 0 3 0.038432 20 0;

2 0 0 3 0.25 20 0;

2 0 0 3 0.01 40 0;

2 0 0 3 0.01 40 0;

2 0 0 3 0.01 40 0;

2 0 0 3 0.01 40 0;

];


return;


% Warnings from cdf2matp conversion:

%

% ***** Qmax = Qmin at generator at bus    1 (Qmax set to Qmin + 10)

% ***** area data conversion not yet implemented (creating dummy area data)

% ***** MVA limit of branch 1 - 2 not given, set to 9900

% ***** MVA limit of branch 1 - 3 not given, set to 9900

% ***** MVA limit of branch 2 - 4 not given, set to 9900

% ***** MVA limit of branch 3 - 4 not given, set to 9900

% ***** MVA limit of branch 2 - 5 not given, set to 9900

% ***** MVA limit of branch 2 - 6 not given, set to 9900

% ***** MVA limit of branch 4 - 6 not given, set to 9900

% ***** MVA limit of branch 5 - 7 not given, set to 9900

% ***** MVA limit of branch 6 - 7 not given, set to 9900

% ***** MVA limit of branch 6 - 8 not given, set to 9900

% ***** MVA limit of branch 6 - 9 not given, set to 9900

% ***** MVA limit of branch 6 - 10 not given, set to 9900

% ***** MVA limit of branch 9 - 11 not given, set to 9900

% ***** MVA limit of branch 9 - 10 not given, set to 9900

% ***** MVA limit of branch 4 - 12 not given, set to 9900

% ***** MVA limit of branch 12 - 13 not given, set to 9900

% ***** MVA limit of branch 12 - 14 not given, set to 9900

% ***** MVA limit of branch 12 - 15 not given, set to 9900

% ***** MVA limit of branch 12 - 16 not given, set to 9900

% ***** MVA limit of branch 14 - 15 not given, set to 9900

% ***** MVA limit of branch 16 - 17 not given, set to 9900

% ***** MVA limit of branch 15 - 18 not given, set to 9900

% ***** MVA limit of branch 18 - 19 not given, set to 9900

% ***** MVA limit of branch 19 - 20 not given, set to 9900

% ***** MVA limit of branch 10 - 20 not given, set to 9900

% ***** MVA limit of branch 10 - 17 not given, set to 9900

% ***** MVA limit of branch 10 - 21 not given, set to 9900

% ***** MVA limit of branch 10 - 22 not given, set to 9900

% ***** MVA limit of branch 21 - 22 not given, set to 9900

% ***** MVA limit of branch 15 - 23 not given, set to 9900

% ***** MVA limit of branch 22 - 24 not given, set to 9900

% ***** MVA limit of branch 23 - 24 not given, set to 9900

% ***** MVA limit of branch 24 - 25 not given, set to 9900

% ***** MVA limit of branch 25 - 26 not given, set to 9900

% ***** MVA limit of branch 25 - 27 not given, set to 9900

% ***** MVA limit of branch 28 - 27 not given, set to 9900

% ***** MVA limit of branch 27 - 29 not given, set to 9900

% ***** MVA limit of branch 27 - 30 not given, set to 9900

% ***** MVA limit of branch 29 - 30 not given, set to 9900

% ***** MVA limit of branch 8 - 28 not given, set to 9900

% ***** MVA limit of branch 6 - 28 not given, set to 9900

function [baseMVA, bus, gen, branch, areas, gencost] = case_ieee30

% CASE_IEEE30    Power flow data for IEEE 30 bus test case.

%   Please see 'help caseformat' for details on the case file format.

%   This data was converted from IEEE Common Data Format

%   (ieee30cdf.txt) on 20-Sep-2004 by cdf2matp, rev. 1.11

%   See end of file for warnings generated during conversion.

%

%   Converted from IEEE CDF file from:

%       http://www.ee.washington.edu/research/pstca/

%

%  08/20/93 UW ARCHIVE           100.0  1961 W IEEE 30 Bus Test Case


%   MATPOWER

%   $Id: case_ieee30.m,v 1.3 2007/09/17 16:07:48 ray Exp $


%%-----  Power Flow Data  -----%%

%% system MVA base

baseMVA = 100;


%% bus data

% bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin

bus = [

1 3 0 0 0 0 1 1.06 0 132 1 1.06 0.94;

2 2 21.7 12.7 0 0 1 1.043 -5.48 132 1 1.06 0.94;

3 1 2.4 1.2 0 0 1 1.021 -7.96 132 1 1.06 0.94;

4 1 7.6 1.6 0 0 1 1.012 -9.62 132 1 1.06 0.94;

5 2 94.2 19 0 0 1 1.01 -14.37 132 1 1.06 0.94;

6 1 0 0 0 0 1 1.01 -11.34 132 1 1.06 0.94;

7 1 22.8 10.9 0 0 1 1.002 -13.12 132 1 1.06 0.94;

8 2 30 30 0 0 1 1.01 -12.1 132 1 1.06 0.94;

9 1 0 0 0 0 1 1.051 -14.38 1 1 1.06 0.94;

10 1 5.8 2 0 19 1 1.045 -15.97 33 1 1.06 0.94;

11 2 0 0 0 0 1 1.082 -14.39 11 1 1.06 0.94;

12 1 11.2 7.5 0 0 1 1.057 -15.24 33 1 1.06 0.94;

13 2 0 0 0 0 1 1.071 -15.24 11 1 1.06 0.94;

14 1 6.2 1.6 0 0 1 1.042 -16.13 33 1 1.06 0.94;

15 1 8.2 2.5 0 0 1 1.038 -16.22 33 1 1.06 0.94;

16 1 3.5 1.8 0 0 1 1.045 -15.83 33 1 1.06 0.94;

17 1 9 5.8 0 0 1 1.04 -16.14 33 1 1.06 0.94;

18 1 3.2 0.9 0 0 1 1.028 -16.82 33 1 1.06 0.94;

19 1 9.5 3.4 0 0 1 1.026 -17 33 1 1.06 0.94;

20 1 2.2 0.7 0 0 1 1.03 -16.8 33 1 1.06 0.94;

21 1 17.5 11.2 0 0 1 1.033 -16.42 33 1 1.06 0.94;

22 1 0 0 0 0 1 1.033 -16.41 33 1 1.06 0.94;

23 1 3.2 1.6 0 0 1 1.027 -16.61 33 1 1.06 0.94;

24 1 8.7 6.7 0 4.3 1 1.021 -16.78 33 1 1.06 0.94;

25 1 0 0 0 0 1 1.017 -16.35 33 1 1.06 0.94;

26 1 3.5 2.3 0 0 1 1 -16.77 33 1 1.06 0.94;

27 1 0 0 0 0 1 1.023 -15.82 33 1 1.06 0.94;

28 1 0 0 0 0 1 1.007 -11.97 132 1 1.06 0.94;

29 1 2.4 0.9 0 0 1 1.003 -17.06 33 1 1.06 0.94;

30 1 10.6 1.9 0 0 1 0.992 -17.94 33 1 1.06 0.94;

];


%% generator data

% bus Pg Qg Qmax Qmin Vg mBase status Pmax Pmin

gen = [

1 260.2 -16.1 10 0 1.06 100 1 360.2 0;

2 40 50 50 -40 1.045 100 1 140 0;

5 0 37 40 -40 1.01 100 1 100 0;

8 0 37.3 40 -10 1.01 100 1 100 0;

11 0 16.2 24 -6 1.082 100 1 100 0;

13 0 10.6 24 -6 1.071 100 1 100 0;

];

x=[1.06

%% branch data

% fbus tbus r x b rateA rateB rateC ratio angle status

branch = [

1 2 0.0192 0.0575 0.0528 9900 0 0 0 0 1;

1 3 0.0452 0.1652 0.0408 9900 0 0 0 0 1;

2 4 0.057 0.1737 0.0368 9900 0 0 0 0 1;

3 4 0.0132 0.0379 0.0084 9900 0 0 0 0 1;

2 5 0.0472 0.1983 0.0418 9900 0 0 0 0 1;

2 6 0.0581 0.1763 0.0374 9900 0 0 0 0 1;

4 6 0.0119 0.0414 0.009 9900 0 0 0 0 1;

5 7 0.046 0.116 0.0204 9900 0 0 0 0 1;

6 7 0.0267 0.082 0.017 9900 0 0 0 0 1;

6 8 0.012 0.042 0.009 9900 0 0 0 0 1;

6 9 0 0.208 0 9900 0 0 0.978 0 1;

6 10 0 0.556 0 9900 0 0 0.969 0 1;

9 11 0 0.208 0 9900 0 0 0 0 1;

9 10 0 0.11 0 9900 0 0 0 0 1;

4 12 0 0.256 0 9900 0 0 0.932 0 1;

12 13 0 0.14 0 9900 0 0 0 0 1;

12 14 0.1231 0.2559 0 9900 0 0 0 0 1;

12 15 0.0662 0.1304 0 9900 0 0 0 0 1;

12 16 0.0945 0.1987 0 9900 0 0 0 0 1;

14 15 0.221 0.1997 0 9900 0 0 0 0 1;

16 17 0.0524 0.1923 0 9900 0 0 0 0 1;

15 18 0.1073 0.2185 0 9900 0 0 0 0 1;

18 19 0.0639 0.1292 0 9900 0 0 0 0 1;

19 20 0.034 0.068 0 9900 0 0 0 0 1;

10 20 0.0936 0.209 0 9900 0 0 0 0 1;

10 17 0.0324 0.0845 0 9900 0 0 0 0 1;

10 21 0.0348 0.0749 0 9900 0 0 0 0 1;

10 22 0.0727 0.1499 0 9900 0 0 0 0 1;

21 22 0.0116 0.0236 0 9900 0 0 0 0 1;

15 23 0.1 0.202 0 9900 0 0 0 0 1;

22 24 0.115 0.179 0 9900 0 0 0 0 1;

23 24 0.132 0.27 0 9900 0 0 0 0 1;

24 25 0.1885 0.3292 0 9900 0 0 0 0 1;

25 26 0.2544 0.38 0 9900 0 0 0 0 1;

25 27 0.1093 0.2087 0 9900 0 0 0 0 1;

28 27 0 0.396 0 9900 0 0 0.968 0 1;

27 29 0.2198 0.4153 0 9900 0 0 0 0 1;

27 30 0.3202 0.6027 0 9900 0 0 0 0 1;

29 30 0.2399 0.4533 0 9900 0 0 0 0 1;

8 28 0.0636 0.2 0.0428 9900 0 0 0 0 1;

6 28 0.0169 0.0599 0.013 9900 0 0 0 0 1;

];


%%-----  OPF Data  -----%%

%% area data

areas = [

1 1;

];


%% generator cost data

% 1 startup shutdown n x1 y1 ... xn yn

% 2 startup shutdown n c(n-1) ... c0

gencost = [

2 0 0 3 0.038432 20 0;

2 0 0 3 0.25 20 0;

2 0 0 3 0.01 40 0;

2 0 0 3 0.01 40 0;

2 0 0 3 0.01 40 0;

2 0 0 3 0.01 40 0;

];


return;


% Warnings from cdf2matp conversion:

%

% ***** Qmax = Qmin at generator at bus    1 (Qmax set to Qmin + 10)

% ***** area data conversion not yet implemented (creating dummy area data)

% ***** MVA limit of branch 1 - 2 not given, set to 9900

% ***** MVA limit of branch 1 - 3 not given, set to 9900

% ***** MVA limit of branch 2 - 4 not given, set to 9900

% ***** MVA limit of branch 3 - 4 not given, set to 9900

% ***** MVA limit of branch 2 - 5 not given, set to 9900

% ***** MVA limit of branch 2 - 6 not given, set to 9900

% ***** MVA limit of branch 4 - 6 not given, set to 9900

% ***** MVA limit of branch 5 - 7 not given, set to 9900

% ***** MVA limit of branch 6 - 7 not given, set to 9900

% ***** MVA limit of branch 6 - 8 not given, set to 9900

% ***** MVA limit of branch 6 - 9 not given, set to 9900

% ***** MVA limit of branch 6 - 10 not given, set to 9900

% ***** MVA limit of branch 9 - 11 not given, set to 9900

% ***** MVA limit of branch 9 - 10 not given, set to 9900

% ***** MVA limit of branch 4 - 12 not given, set to 9900

% ***** MVA limit of branch 12 - 13 not given, set to 9900

% ***** MVA limit of branch 12 - 14 not given, set to 9900

% ***** MVA limit of branch 12 - 15 not given, set to 9900

% ***** MVA limit of branch 12 - 16 not given, set to 9900

% ***** MVA limit of branch 14 - 15 not given, set to 9900

% ***** MVA limit of branch 16 - 17 not given, set to 9900

% ***** MVA limit of branch 15 - 18 not given, set to 9900

% ***** MVA limit of branch 18 - 19 not given, set to 9900

% ***** MVA limit of branch 19 - 20 not given, set to 9900

% ***** MVA limit of branch 10 - 20 not given, set to 9900

% ***** MVA limit of branch 10 - 17 not given, set to 9900

% ***** MVA limit of branch 10 - 21 not given, set to 9900

% ***** MVA limit of branch 10 - 22 not given, set to 9900

% ***** MVA limit of branch 21 - 22 not given, set to 9900

% ***** MVA limit of branch 15 - 23 not given, set to 9900

% ***** MVA limit of branch 22 - 24 not given, set to 9900

% ***** MVA limit of branch 23 - 24 not given, set to 9900

% ***** MVA limit of branch 24 - 25 not given, set to 9900

% ***** MVA limit of branch 25 - 26 not given, set to 9900

% ***** MVA limit of branch 25 - 27 not given, set to 9900

% ***** MVA limit of branch 28 - 27 not given, set to 9900

% ***** MVA limit of branch 27 - 29 not given, set to 9900

% ***** MVA limit of branch 27 - 30 not given, set to 9900

% ***** MVA limit of branch 29 - 30 not given, set to 9900

% ***** MVA limit of branch 8 - 28 not given, set to 9900

% ***** MVA limit of branch 6 - 28 not given, set to 9900

⛄ 运行结果

⛄ 参考文献

[1] 董淑华.基于改进粒子群优化算法的电力系统无功优化[D].中南大学,2010.DOI:10.7666/d.y1719172.

[2] 陶国正,徐志成.基于粒子群优化算法的电力系统无功优化[J].计算机工程, 2010, 36(20):3.DOI:10.3969/j.issn.1000-3428.2010.20.069.

[3] 耿彦波.基于粒子群算法的电力系统无功优化[D].华北电力大学(北京),2009.DOI:10.7666/d.D518246.

[4] 周敏,王劲草,吴刚.基于粒子群算法的电力系统无功优化[J].电工技术, 2014, 000(005):34-35.DOI:10.3969/j.issn.1002-1388.2014.05.018.

⛳️ 代码获取关注我

❤️部分理论引用网络文献,若有侵权联系博主删除
❤️ 关注我领取海量matlab电子书和数学建模资料



相关文章
|
2天前
|
机器学习/深度学习 算法
基于改进遗传优化的BP神经网络金融序列预测算法matlab仿真
本项目基于改进遗传优化的BP神经网络进行金融序列预测,使用MATLAB2022A实现。通过对比BP神经网络、遗传优化BP神经网络及改进遗传优化BP神经网络,展示了三者的误差和预测曲线差异。核心程序结合遗传算法(GA)与BP神经网络,利用GA优化BP网络的初始权重和阈值,提高预测精度。GA通过选择、交叉、变异操作迭代优化,防止局部收敛,增强模型对金融市场复杂性和不确定性的适应能力。
104 80
|
2天前
|
缓存 算法 搜索推荐
Java中的算法优化与复杂度分析
在Java开发中,理解和优化算法的时间复杂度和空间复杂度是提升程序性能的关键。通过合理选择数据结构、避免重复计算、应用分治法等策略,可以显著提高算法效率。在实际开发中,应该根据具体需求和场景,选择合适的优化方法,从而编写出高效、可靠的代码。
15 6
|
8天前
|
机器学习/深度学习 前端开发 算法
婚恋交友系统平台 相亲交友平台系统 婚恋交友系统APP 婚恋系统源码 婚恋交友平台开发流程 婚恋交友系统架构设计 婚恋交友系统前端/后端开发 婚恋交友系统匹配推荐算法优化
婚恋交友系统平台通过线上互动帮助单身男女找到合适伴侣,提供用户注册、个人资料填写、匹配推荐、实时聊天、社区互动等功能。开发流程包括需求分析、技术选型、系统架构设计、功能实现、测试优化和上线运维。匹配推荐算法优化是核心,通过用户行为数据分析和机器学习提高匹配准确性。
37 3
|
7天前
|
供应链 算法 调度
排队算法的matlab仿真,带GUI界面
该程序使用MATLAB 2022A版本实现排队算法的仿真,并带有GUI界面。程序支持单队列单服务台、单队列多服务台和多队列多服务台三种排队方式。核心函数`func_mms2`通过模拟到达时间和服务时间,计算阻塞率和利用率。排队论研究系统中顾客和服务台的交互行为,广泛应用于通信网络、生产调度和服务行业等领域,旨在优化系统性能,减少等待时间,提高资源利用率。
|
8天前
|
算法
PAI下面的gbdt、xgboost、ps-smart 算法如何优化?
设置gbdt 、xgboost等算法的样本和特征的采样率
23 2
|
14天前
|
存储 算法
基于HMM隐马尔可夫模型的金融数据预测算法matlab仿真
本项目基于HMM模型实现金融数据预测,包括模型训练与预测两部分。在MATLAB2022A上运行,通过计算状态转移和观测概率预测未来值,并绘制了预测值、真实值及预测误差的对比图。HMM模型适用于金融市场的时间序列分析,能够有效捕捉隐藏状态及其转换规律,为金融预测提供有力工具。
|
14天前
|
机器学习/深度学习 算法 信息无障碍
基于GoogleNet深度学习网络的手语识别算法matlab仿真
本项目展示了基于GoogleNet的深度学习手语识别算法,使用Matlab2022a实现。通过卷积神经网络(CNN)识别手语手势,如"How are you"、"I am fine"、"I love you"等。核心在于Inception模块,通过多尺度处理和1x1卷积减少计算量,提高效率。项目附带完整代码及操作视频。
|
17天前
|
机器学习/深度学习 算法 数据安全/隐私保护
基于深度学习网络的宝石类型识别算法matlab仿真
本项目利用GoogLeNet深度学习网络进行宝石类型识别,实验包括收集多类宝石图像数据集并按7:1:2比例划分。使用Matlab2022a实现算法,提供含中文注释的完整代码及操作视频。GoogLeNet通过其独特的Inception模块,结合数据增强、学习率调整和正则化等优化手段,有效提升了宝石识别的准确性和效率。
|
1天前
|
算法
基于EO平衡优化器算法的目标函数最优值求解matlab仿真
本程序基于进化优化(EO)中的平衡优化器算法,在MATLAB2022A上实现九个测试函数的最优值求解及优化收敛曲线仿真。平衡优化器通过模拟生态系统平衡机制,动态调整搜索参数,确保种群多样性与收敛性的平衡,高效搜索全局或近全局最优解。程序核心为平衡优化算法,结合粒子群优化思想,引入动态调整策略,促进快速探索与有效利用解空间。
|
21天前
|
算法
基于WOA算法的SVDD参数寻优matlab仿真
该程序利用鲸鱼优化算法(WOA)对支持向量数据描述(SVDD)模型的参数进行优化,以提高数据分类的准确性。通过MATLAB2022A实现,展示了不同信噪比(SNR)下模型的分类误差。WOA通过模拟鲸鱼捕食行为,动态调整SVDD参数,如惩罚因子C和核函数参数γ,以寻找最优参数组合,增强模型的鲁棒性和泛化能力。