PlatEMO/Problems/Multi-objective optimization/ZDT/ZDT6.m
classdef ZDT6 < PROBLEM
% <2000> <multi> <real> <large/none> <expensive/none>
% Benchmark MOP proposed by Zitzler, Deb, and Thiele

%------------------------------- Reference --------------------------------
% E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective
% evolutionary algorithms: Empirical results. Evolutionary Computation,
% 2000, 8(2): 173-195.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2025 BIMK Group. You are free to use the PlatEMO for
% research purposes. All publications which use this platform or any code
% in the platform should acknowledge the use of "PlatEMO" and reference "Ye
% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform
% for evolutionary multi-objective optimization [educational forum], IEEE
% Computational Intelligence Magazine, 2017, 12(4): 73-87".
%--------------------------------------------------------------------------

    methods
        %% Default settings of the problem
        function Setting(obj)
            obj.M = 2;
            if isempty(obj.D); obj.D = 10; end
            obj.lower    = zeros(1,obj.D);
            obj.upper    = ones(1,obj.D);
            obj.encoding = ones(1,obj.D);
        end
        %% Calculate objective values
        function PopObj = CalObj(obj,PopDec)
            PopObj(:,1) = 1 - exp(-4*PopDec(:,1)).*sin(6*pi*PopDec(:,1)).^6;
            g = 1 + 9*mean(PopDec(:,2:end),2).^0.25;
            h = 1 - (PopObj(:,1)./g).^2;
            PopObj(:,2) = g.*h;
        end
        %% Generate points on the Pareto front
        function R = GetOptimum(obj,N)
            minf1  = 0.280775;
            R(:,1) = linspace(minf1,1,N)';
            R(:,2) = 1 - R(:,1).^2;
        end
        %% Generate the image of Pareto front
        function R = GetPF(obj)
            R = obj.GetOptimum(100);
        end
    end
end