OptimizationValues
创建对象
solve
函数返回 OptimizationValues
对象的向量作为多目标问题的解。
使用 optimvalues
函数为起点 x0
创建一个 OptimizationValues
对象。
属性
通常,OptimizationValues
属性是动态的:它们是优化变量、目标函数和约束的名称。
但是,您也可以有未命名的目标函数或约束。对于这些情况,OptimizationValues
会对以下属性赋值。
Objective
— 目标函数值
实数数组
目标函数值,以实数数组形式返回或指定。
数据类型: double
Constraints
— 约束值
实数数组
约束值,以实数数组形式返回或指定。
数据类型: double
对象函数
paretoplot | Pareto plot of multiobjective values |
示例
OptimizationValues
保留多目标问题的解
使用优化变量创建和求解一个多目标问题。
x = optimvar("x",LowerBound=-3,UpperBound=3); prob = optimproblem; prob.Objective = [x^2;(x-1)^2]; % Tradeoff region between x = 0 and x = 1 prob.Constraints.con1 = x^2 <= 1/2; % Demonstrate constraints prob.Constraints.con2 = x^2 >= 1/10; % Second constraint rng default % For reproducibility [sol,fval,exitflag,output] = solve(prob,Solver="paretosearch")
Solving problem using paretosearch. Pareto set found that satisfies the constraints. Optimization completed because the relative change in the volume of the Pareto set is less than 'options.ParetoSetChangeTolerance' and constraints are satisfied to within 'options.ConstraintTolerance'.
sol = 1x60 OptimizationValues vector with properties: Variables properties: x: [0.7027 0.7014 0.3635 0.3491 0.5723 0.3177 0.4634 0.4400 0.6379 0.6402 0.3750 0.6057 0.6565 0.5483 0.6455 0.5518 0.3760 0.3232 0.6768 0.6357 0.5625 0.3952 0.3382 0.3857 0.5677 0.5170 0.5107 0.6241 0.6615 0.3295 0.6875 ... ] (1x60 double) Objective properties: Objective: [2x60 double] Constraints properties: con1: [-0.0063 -0.0081 -0.3679 -0.3781 -0.1725 -0.3991 -0.2853 -0.3064 -0.0931 -0.0902 -0.3594 -0.1332 -0.0690 -0.1994 -0.0833 -0.1955 -0.3586 -0.3955 -0.0420 -0.0959 -0.1836 -0.3438 -0.3856 -0.3512 -0.1777 -0.2327 -0.2392 ... ] (1x60 double) con2: [-0.3937 -0.3919 -0.0321 -0.0219 -0.2275 -9.3572e-04 -0.1147 -0.0936 -0.3069 -0.3098 -0.0406 -0.2668 -0.3310 -0.2006 -0.3167 -0.2045 -0.0414 -0.0045 -0.3580 -0.3041 -0.2164 -0.0562 -0.0144 -0.0488 -0.2223 -0.1673 -0.1608 ... ] (1x60 double)
fval = 2×60
0.4937 0.4919 0.1321 0.1219 0.3275 0.1009 0.2147 0.1936 0.4069 0.4098 0.1406 0.3668 0.4310 0.3006 0.4167 0.3045 0.1414 0.1045 0.4580 0.4041 0.3164 0.1562 0.1144 0.1488 0.3223 0.2673 0.2608 0.3895 0.4375 0.1086 0.4727 0.2001 0.4395 0.2261 0.1658 0.4862 0.4270 0.3525 0.1057 0.2628 0.2197 0.4902 0.1760 0.3665 0.2411 0.1092 0.4911 0.1914 0.1182 0.3742
0.0884 0.0892 0.4051 0.4237 0.1829 0.4655 0.2880 0.3136 0.1311 0.1295 0.3906 0.1555 0.1180 0.2040 0.1257 0.2009 0.3893 0.4580 0.1045 0.1327 0.1914 0.3658 0.4380 0.3773 0.1869 0.2333 0.2394 0.1413 0.1146 0.4496 0.0977 0.3055 0.1136 0.2751 0.3514 0.0916 0.1201 0.1650 0.4555 0.2375 0.2822 0.0899 0.3369 0.1557 0.2591 0.4483 0.0895 0.3164 0.4307 0.1508
exitflag = SolverConvergedSuccessfully
output = struct with fields:
iterations: 20
funccount: 380
volume: 1.8611
averagedistance: 0.0101
spread: 0.3067
maxconstraint: 0
message: 'Pareto set found that satisfies the constraints. ...'
rngstate: [1x1 struct]
solver: 'paretosearch'
paretosearch
求解器经过 16 次迭代收敛于一个可行解。绘制解。
paretoplot(sol)
使用数据提示选择图中的一个任意点进行检查:
图中的点位于索引 48 处。检查解 48。
arbitrarysol = sol(48)
arbitrarysol = OptimizationValues with properties: Variables properties: x: 0.4375 Objective properties: Objective: [2x1 double] Constraints properties: con1: -0.3086 con2: -0.0914
约束值为负,表示图中的点可行。
arbitrarysol.Objective
ans = 2×1
0.1914
0.3164
目标值与数据提示中的值匹配。
局限性
OptimizationValues
对象仅支持水平串联。换句话说,您只能有由OptimizationValues
对象组成的行向量。
版本历史记录
在 R2022a 中推出
另请参阅
主题
- Specify Start Points for MultiStart, Problem-Based (Global Optimization Toolbox)
- Pareto Front for Multiobjective Optimization, Problem-Based (Global Optimization Toolbox)
MATLAB 命令
您点击的链接对应于以下 MATLAB 命令:
请在 MATLAB 命令行窗口中直接输入以执行命令。Web 浏览器不支持 MATLAB 命令。
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)