遺傳算法多目標函數(shù)優(yōu)化

1、. -多目標遺傳算法優(yōu)化銑削正交試驗結果序號vc(m/min)fz(mm/z)ae(mm)ap(mm)F(N)Ra(m)1400.020.25446.30.1742400.040.5659.70.2113400.060.758106.50.2884400.081.010123.00.3485600.020.25899.30.2526600.040.510134.00.3027600.061.04139.70.2388600.080.756130.40.2769800.020.7510255.80.29810800.041.08255.80.28811800.060.256110.40.2591

2、2800.080.54140.80.258131000.021.06314.20.240141000.040.754233.40.239151000.060.510278.60.330161000.080.258144.70.328說明：1.建立切削力和外表粗糙度模型如：1此模型你們來擬合上面有實驗數(shù)據(jù)，剩下的兩個方程已經(jīng)是我?guī)湍銈償M合好的了23變量約束圍：公式1和2值越小越好，公式3值越大越好。=3.14 D=82.請將多目標優(yōu)化操作過程錄像同時考慮三個方程，優(yōu)化出最優(yōu)的自變量數(shù)值，方便我后續(xù)進展修改；將能保存的所有圖片及源文件發(fā)給我；將最優(yōu)解多組發(fā)給我，類似于下列圖黃色局部為到達的要求遺傳

3、算法的結果:vcfzaeapFRaQ69.49510.02010.59362.030144.45690.1507-133.92377.76040.080.80047.7189298.80560.3241-3058.3469.84320.04290.79237.316175.06530.2827-1383.0170.2130.02820.69457.6443117.280.2673-837.41369.49510.02010.59362.030144.45690.1507-133.923程序如下:clear; clc;% 遺傳算法直接求解多目標優(yōu)化D=8;% Function handle to

4、 the fitness functionF=(X)10(3.19)*(X(1).(-0.0836).*(X(2).0.825).*(X(3).0.564).*(X(4).0.454);Ra=(X)10(-0.92146)*(X(1).0.14365).*(X(2).0.16065).*(X(3).0.047691).*(X(4).0.38457);Q=(X)-1000*2*X(1).*X(2).*X(3).*X(4)/(pi*D); nvars = 4; % Number of decision variableslb = 40, 0.02,0.25, 2; % Lower boundub

5、= 100,0.08, 1.0,10; % Upper boundA = ; b = ; % No linear inequality constraintsAeq = ; beq = ; % No linear equality constraints% 遺傳算法設定約束options = gaoptimset('ParetoFraction',0.3,'PopulationSize',200,'Generations',300,'StallGenLimit',200,'TolFun',1e-100,'P

6、lotFs',gaplotpareto);% the data of Pareto1FUN=(X)F(X);Ra(X);x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個新的圖形figure;% 畫出Pareto1plot(fval(:,1),fval(:,2),'mp');% 畫出網(wǎng)格grid on;% 設定橫坐標xlabel('F');% 設定縱坐標ylabel('Ra');% 設定題目title('Pareto front 1');% the

7、 data of Pareto2FUN=(X)F(X);Q(X);x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個新的圖形figure;% 畫出Pareto2plot(fval(:,1),fval(:,2),'mp');% 畫出網(wǎng)格grid on;% 設定橫坐標xlabel('F');% 設定縱坐標ylabel('Q');% 設定題目title('Pareto front 2');% the data of Pareto3FUN=(X)Ra(X);Q(X

8、);x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個新的圖形figure;% 畫出Pareto3plot(fval(:,1),fval(:,2),'mp');% 畫出網(wǎng)格grid on;% 設定橫坐標xlabel('Ra');% 設定縱坐標ylabel('Q');% 設定題目title('Pareto front 3');% the data of Pareto (F,Ra,Q)FUN=(X)F(X);Ra(X);Q(X);x,fval = gamult

9、iobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個新的圖形figure;% 畫出Pareto4plot3(fval(:,1),fval(:,2),fval(:,3),'m.');% 畫出網(wǎng)格grid on;% 設定橫坐標xlabel('F');% 設定縱坐標ylabel('Ra');% 設定豎坐標zlabel('Q');% 設定題目title('Pareto front 4');% 重新設定遺傳算法options = gaoptimset('ParetoFraction',0.3,'PopulationSize',5,'Generations',1000,'StallGenLimit',200,'TolFun',1e-100,'PlotFs',gaplotpareto);% 遺傳算法求解x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options