遗传算法多目标函数优化

的血緒搠I的血緒搠I血EW目牺上一：威I■于tftMaUvDTlllfc...'；卿■于d!i总比K甜惴宦岛礼」煽9■干孑目I谄MK甜擁氓1多目标遗传算法优化铣削正交试验结果序号(m/min)fz(mm/z)a(mm)ap(mm)F(N)Ra(卩m)1400.020.25446.30.1742400.040.5659.70.2113400.060.758106.50.2884400.081.010123.00.3485600.020.25899.30.2526600.040.510134.00.3027600.061.04139.70.2388600.080.756130.40.2769800.020.7510255.80.29810800.041.08255.80.28811800.060.256110.40.25912800.080.54140.80.258131000.021.06314.20.240141000.040.754233.40.239151000.060.510278.60.330161000.080.258144.70.328说明：如：1）1.如：1）F=103.19V€0.0836f0.825a0.564a0.454czepRa二此模型你们来拟合(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)Ra二此模型你们来拟合(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)R=10-0.92146匕0.14365广0.16065口0.047691口0.38457

ac£pQ=一1000-2v-f-a-a/，Dczpe40„v„100c0.02„f„0.08z0.25„a„1.0e2„a„10p变量约束范围：＜2）（3）公式（1）和（2）值越小越好，公式（3）值越大越好°n=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），方便我后续进行修改；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，类似于下图（黄色部分为达到的要求）

削佃度牧高•导致刀貝寿命变短#KWiCm/'min)iS蜡亘(Hum丿r>丹吃刀ssCliilil、切削竝材鶴誉棣申Asa.sr&iO.ftSJ(&.22I.3SL27L534.534-sa.srwKWiCm/'min)iS蜡亘(Hum丿r>丹吃刀ssCliilil、切削竝材鶴誉棣申Asa.sr&iO.ftSJ(&.22I.3SL27L534.534-sa.srwO.ftSJR.629-3SH.62fiIW-29752.9W(J.-074*.97&屯H.别？3K55.9lhH54.了54G1470.97543y.M77827.59*13&.6J7G1430.9W)447.T»MUZ5.535*57.17?a聊59S445I.9MI*83*7.477C7L.49ftai的攸轴517.914L-D60Q..H-L7SL.2R6ai刃(k966560L21.01.1.752.M684.7H50.149I..CH冊574.6M4I2jti53.4fi7rti丁切闸連度对切削蛊度廳响逸明屜曲*rti区1U4-farF-.f±--=YnljfiLlidImd:.li±cU.tf±:AJL_L.JU512耗橇匿与时吗圭脱电狀忧Farfl佃苛沿

表3邯分胳的皿恒IX城UQHIO[niJtfiin?进拾晟<miii^r)IflOfl：(■Wl!去Iffi机卷攪村■料去险率A32.Sliy0.130(h/KJ0.7336I77.MW57.J340.JJI0.甥了0.75»73H2.fiS75S.5720.J320.w0.7677553.A6JIf6i2.?100・131CJ.77fr艸购.1到ftZ.SZJ0.131o.m(J.770SII5.2剳62,0440.124CL屈S253.503£fi2,69fi0.MH0.H754261.L97ftZ.7350.ISO0.W9ClW93鹳.刃】遗传算法的结果:Paretofront1

Paretofront2Paretofront3RaParetofront4030020021000.1Paretofront4030020021000.10-2000-4000-6000-80000.4VCfzaeapFRaQ69.49510.02010.59362.030144.45690.1507-133.92377.76040.080.80047.7189298.80560.3241-3058.3469.84320.04290.79237.316175.06530.2827-1383.0170.2130.02820.69457.6443117.280.2673-837.41369.49510.02010.59362.030144.45690.1507-133.9230程序如下:clear;clc;%遗传算法直接求解多目标优化D=8;%FunctionhandletothefitnessfunctionF=@(X)[10,3.19)*(X(l)J(—O.O836)).*(X(2).P.825).*(X(3)「O.564).*(X(4).P.454)];Ra=@(X)[10(O.92146)*(X(1)「O.14365).*(X(2).P.16O65).*(X(3)「O.O47691).*(X(4).P.38457)];Q=@(X)[-1000*2*X(1).*X(2).*X(3).*X(4)/(pi*D)];nvars=4;%Numberofdecisionvariableslb=[40,0.02,0.25,2];%Lowerboundub=[100,0.08,1.0,10];%UpperboundA=[];b=[];%NolinearinequalityconstraintsAeq=[];beq=[];%Nolinearequalityconstraints%遗传算法设定约束options=gaoptimset('ParetoFraction',0.3,'PopulationSize',200,'Generations',300,'StallGenLimit',200,'TolFun',1e-100,'PlotFcns',@gaplotpareto);%thedataofPareto1FUN=@(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Paretolplot(fval(:,1),fval(:,2),'mp');%画出网格gridon;%设定横坐标xlabel('F');%设定纵坐标ylabel('Ra');%设定题目title('Paretofrontl');%thedataofPareto2FUN=@(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(:,l),fval(:,2),'mp');%画出网格gridon;%设定横坐标xlabel('F');%设定纵坐标ylabel('Q');%设定题目title('Paretofront2');%thedataofPareto3FUN=@(X)[Ra(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Pareto3plot(fval(:,1),fval(:,2),'mp');%画出网格gridon;%设定横坐标xlabel('Ra');%设定纵坐标ylabel('Q');%设定题目title('Paretofront3');%thedataofPareto(F,Ra,Q)FUN=@(X)[F(X);Ra(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gridon;%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('Paretofront4');%重新设定遗传算法options=gaoptimset('ParetoFraction',0.3,'PopulationSize',5,'Generations',1000,'StallGenLimit',200,'TolFun',1e-100,'PlotFcns