遗传算法多目标函数优化

1、WORD格式多目标遗传算法优化铣削正交试验结果序vcfzaeapFRa号(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说明：1.建立切削力和外表粗糙度模型如： F103.19 vc0.0836 fz0.825ae0.564ap0.454 1Ra此模型你们来拟合上面有实验数据，剩下的两个方程已经是我帮你们拟合好的了 2""=10- 0.921460.143650.160650.0476910.38457""""&quo

3、t;"""""""""""Q 1000 2vc fz ap ae / D 3 40 vc 1000.02fz0.08变量约束X围：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.请将多目标优化操作过程录像同时考虑三个方程，优化出最优的自变量数值，方便我后续进展修改； 将能保存的所有图片及源文件发给我；将最优解多组发给我， 类似于以下图黄色局部为到达的要求专业资料整理WORD格式遗传算法的结果 :专业资料整理WORD格式专业资料整理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;% 遗传算法直接求解多目标优化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);专业资料整理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% 遗传算法设定约束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);% 开启一个新的图形figure;% 画出 Pareto1 plot(fval(:,1),fval(:,2),'mp');% 画出网格grid on;% 设定横坐标xlabel('F');% 设定纵坐标ylabel('Ra');% 设定题目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);% 开启一个新的图形figure;% 画出 Pareto2plot(fval(:,1),fval(:,2),'mp');% 画出网格grid on;% 设定横坐标xlabel('F');% 设定纵坐标ylabel('Q');% 设定题目title('Pareto front 2');% the data of Pareto3FUN=(X

9、)Ra(X);Q(X);专业资料整理WORD格式x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub,options);% 开启一个新的图形figure;% 画出 Pareto3 plot(fval(:,1),fval(:,2),'mp');% 画出网格grid on;% 设定横坐标xlabel('Ra');% 设定纵坐标ylabel('Q');% 设定题目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);% 开启一个新的图形figure;% 画出 Pareto4plot3(fval(:,1),fval(:,2),fval(:,3),'m.');% 画出网格grid on;% 设定横坐标xlabel('F');% 设定纵坐标ylabel('Ra');% 设定竖坐标zlabel('Q');% 设定题目title('Pareto front 4');% 重新设定遗传算法options =gaoptimset('ParetoFraction',0.3,'PopulationSize',5,'Generations',1000,'StallGenLimit',200,'TolFun',1e-100,'PlotFcns',gaplotpareto);% 遗传算法求解x,fval = gamultiobj(FUN,nvars,A,b,Aeq,beq,lb,ub