matlab智能算法——chapter9 基于遗传算法的多目标优化算法.rar

clear clc fitnessfcn = @my_first_multi; % Function handle to the fitness function nvars = 2; % Number of decision variables lb = [-5,-5]; % Lower bound ub = [5,5]; % Upper bound A = []; b = []; % No linear inequality constraints Aeq = []; beq = []; % No linear equality constraints options = gaoptimset('ParetoFraction',0.3,'PopulationSize',100,'Generations',200,'StallGenLimit',200,'TolFun',1e-100,'PlotFcns',@gaplotpareto); [x,fval] = gamultiobj(fitnessfcn,nvars, A,b,Aeq,beq,lb,ub,options);