遺傳算法多目標(biāo)函數(shù)優(yōu)化

1、WORD格式多目標(biāo)遺傳算法優(yōu)化銑削正交試驗(yàn)結(jié)果序vcfzaeapFRa號(hào)(m/min)(mm/z)(mm)(mm)(N)(m)1400.020.25446.30.1742400.040.5659.70.2113400.060.758106.50.2884400.081.010123.00.3485600.020.25899.30.2526600.040.510134.00.3027600.061.04139.70.2388600.080.756130.40.2769800.020.7510255.80.29810800.041.08255.80.28811800.060.256110.40.2

2、5912800.080.54140.80.258131000.021.06314.20.240141000.040.754233.40.239151000.060.510278.60.330161000.080.258144.70.328說明：1.建立切削力和外表粗糙度模型如： F103.19 vc0.0836 fz0.825ae0.564ap0.454 1Ra此模型你們來擬合上面有實(shí)驗(yàn)數(shù)據(jù)，剩下的兩個(gè)方程已經(jīng)是我?guī)湍銈償M合好的了 2""=10- 0.921460.143650.160650.0476910.38457""""&quo

3、t;"""""""""""Q 1000 2vc fz ap ae / D 3 40 vc 1000.02fz0.08變量約束X圍：ae1.00.252 a p10公式 1和 2值越小越好，公式 3值越大越好。 =3.14D=82.請(qǐng)將多目標(biāo)優(yōu)化操作過程錄像同時(shí)考慮三個(gè)方程，優(yōu)化出最優(yōu)的自變量數(shù)值，方便我后續(xù)進(jìn)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， 類似于以下圖黃色局部為到達(dá)的要求專業(yè)資料整理WORD格式遺傳算法的結(jié)果 :專業(yè)資料整理WORD格式專業(yè)資料整理WO

4、RD格式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(biāo)優(yōu)化D=8;% Function handle to

5、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);專業(yè)資料整理WORD格式nvars = 4;% Number of decision variableslb = 40, 0.02,0.25, 2;% Lower

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

7、39;PlotFcns',gaplotpareto);% the data of Pareto1 FUN=(X)F(X);Ra(X);x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個(gè)新的圖形figure;% 畫出 Pareto1 plot(fval(:,1),fval(:,2),'mp');% 畫出網(wǎng)格grid on;% 設(shè)定橫坐標(biāo)xlabel('F');% 設(shè)定縱坐標(biāo)ylabel('Ra');% 設(shè)定題目title('Pareto front 1

8、9;);% the data of Pareto2FUN=(X)F(X);Q(X);x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個(gè)新的圖形figure;% 畫出 Pareto2plot(fval(:,1),fval(:,2),'mp');% 畫出網(wǎng)格grid on;% 設(shè)定橫坐標(biāo)xlabel('F');% 設(shè)定縱坐標(biāo)ylabel('Q');% 設(shè)定題目title('Pareto front 2');% the data of Pareto3FUN=(X

9、)Ra(X);Q(X);專業(yè)資料整理WORD格式x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 開啟一個(gè)新的圖形figure;% 畫出 Pareto3 plot(fval(:,1),fval(:,2),'mp');% 畫出網(wǎng)格grid on;% 設(shè)定橫坐標(biāo)xlabel('Ra');% 設(shè)定縱坐標(biāo)ylabel('Q');% 設(shè)定題目title('Pareto front 3');% the data of Pareto (F,Ra,Q) FUN=(X)F(X);R

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