1. Optimize the GUI and fix bugs.

2. add New Algorithms and Problems

PlatEMO/Algorithms/MOEA-D-DE/MOEADDE.m

@@ -39,7 +39,7 @@ function MOEADDE(Global)
         for i = 1 : Global.N
             % Choose the parents
             if rand < delta
-                P = B(i,randperm(size(B,2)));
+                P = B(i,randperm(end));
             else
                 P = randperm(Global.N);
             end

PlatEMO/Algorithms/MOEA-D-DRA/MOEADDRA.m

@@ -46,7 +46,7 @@ function MOEADDRA(Global)
             for i = I
                 % Choose the parents
                 if rand < 0.9
-                    P = B(i,randperm(size(B,2)));
+                    P = B(i,randperm(end));
                 else
                     P = randperm(Global.N);
                 end

PlatEMO/Algorithms/SparseEA/EnvironmentalSelection.m

@@ -0,0 +1,38 @@
+function [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection(Population,Dec,Mask,N)
+% The environmental selection of SparseEA
+
+%------------------------------- Copyright --------------------------------
+% Copyright (c) 2018-2019 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".
+%--------------------------------------------------------------------------
+
+    %% Delete duplicated solutions
+    [~,uni] = unique(Population.objs,'rows');
+    Population = Population(uni);
+    Dec        = Dec(uni,:);
+    Mask       = Mask(uni,:);
+    N          = min(N,length(Population));
+
+    %% Non-dominated sorting
+    [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N);
+    Next = FrontNo < MaxFNo;
+
+    %% Calculate the crowding distance of each solution
+    CrowdDis = CrowdingDistance(Population.objs,FrontNo);
+
+    %% Select the solutions in the last front based on their crowding distances
+    Last     = find(FrontNo==MaxFNo);
+    [~,Rank] = sort(CrowdDis(Last),'descend');
+    Next(Last(Rank(1:N-sum(Next)))) = true;
+
+    %% Population for next generation
+    Population = Population(Next);
+    FrontNo    = FrontNo(Next);
+    CrowdDis   = CrowdDis(Next);
+    Dec        = Dec(Next,:);
+    Mask       = Mask(Next,:);
+end

PlatEMO/Algorithms/SparseEA/Operator.m

@@ -0,0 +1,61 @@
+function [OffDec,OffMask] = Operator(ParentDec,ParentMask,Fitness,REAL)
+% The operator of SparseEA
+
+%------------------------------- Copyright --------------------------------
+% Copyright (c) 2018-2019 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".
+%--------------------------------------------------------------------------
+
+    %% Parameter setting
+    [N,D]       = size(ParentDec);
+    Parent1Mask = ParentMask(1:N/2,:);
+    Parent2Mask = ParentMask(N/2+1:end,:);
+
+    %% Crossover for mask
+    OffMask = Parent1Mask;
+    for i = 1 : N/2
+        if rand < 0.5
+            index = find(Parent1Mask(i,:)&~Parent2Mask(i,:));
+            index = index(TS(-Fitness(index)));
+            OffMask(i,index) = 0;
+        else
+            index = find(~Parent1Mask(i,:)&Parent2Mask(i,:));
+            index = index(TS(Fitness(index)));
+            OffMask(i,index) = Parent2Mask(i,index);
+        end
+    end
+
+    %% Mutation for mask
+    for i = 1 : N/2
+        if rand < 0.5
+            index = find(OffMask(i,:));
+            index = index(TS(-Fitness(index)));
+            OffMask(i,index) = 0;
+        else
+            index = find(~OffMask(i,:));
+            index = index(TS(Fitness(index)));
+            OffMask(i,index) = 1;
+        end
+    end
+
+    %% Crossover and mutation for dec
+    if REAL
+        OffDec = GAhalf(ParentDec);
+    else
+        OffDec = ones(N/2,D);
+    end
+end
+
+function index = TS(Fitness)
+% Binary tournament selection
+
+    if isempty(Fitness)
+        index = [];
+    else
+        index = TournamentSelection(2,1,Fitness);
+    end
+end

PlatEMO/Algorithms/SparseEA/SparseEA.m

@@ -0,0 +1,58 @@
+function SparseEA(Global)
+% <algorithm> <S>
+% Evolutionary algorithm for sparse multi-objective optimization problems
+
+%------------------------------- Reference --------------------------------
+% Y. Tian, X. Zhang, C. Wang, and Y. Jin, An evolutionary algorithm for
+% large-scale sparse multi-objective optimization problems, IEEE
+% Transactions on Evolutionary Computation, 2019.
+%------------------------------- Copyright --------------------------------
+% Copyright (c) 2018-2019 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".
+%--------------------------------------------------------------------------
+
+    %% Population initialization
+    % Calculate the fitness of each decision variable
+    TDec    = [];
+    TMask   = [];
+    TempPop = [];
+    Fitness = zeros(1,Global.D);
+    REAL    = ~strcmp(Global.encoding,'binary');
+    for i = 1 : 1+4*REAL
+        if REAL
+            Dec = unifrnd(repmat(Global.lower,Global.D,1),repmat(Global.upper,Global.D,1));
+        else
+            Dec = ones(Global.D,Global.D);
+        end
+        Mask       = eye(Global.D);
+        Population = INDIVIDUAL(Dec.*Mask);
+        TDec       = [TDec;Dec];
+        TMask      = [TMask;Mask];
+        TempPop    = [TempPop,Population];
+        Fitness    = Fitness + NDSort([Population.objs,Population.cons],inf);
+    end
+    % Generate initial population
+    if REAL
+        Dec = unifrnd(repmat(Global.lower,Global.N,1),repmat(Global.upper,Global.N,1));
+    else
+        Dec = ones(Global.N,Global.D);
+    end
+    Mask = zeros(Global.N,Global.D);
+    for i = 1 : Global.N
+        Mask(i,TournamentSelection(2,ceil(rand*Global.D),Fitness)) = 1;
+    end
+    Population = INDIVIDUAL(Dec.*Mask);
+    [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection([Population,TempPop],[Dec;TDec],[Mask;TMask],Global.N);
+
+    %% Optimization
+    while Global.NotTermination(Population)
+        MatingPool       = TournamentSelection(2,2*Global.N,FrontNo,-CrowdDis);
+        [OffDec,OffMask] = Operator(Dec(MatingPool,:),Mask(MatingPool,:),Fitness,REAL);
+        Offspring        = INDIVIDUAL(OffDec.*OffMask);
+        [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection([Population,Offspring],[Dec;OffDec],[Mask;OffMask],Global.N);
+    end
+end

PlatEMO/GUI/Controls/newAxes.m

@@ -72,10 +72,10 @@ function draw(obj,Data,varargin)
             if ~obj.viewFix
                 if M == 2
                     view(0,90);
-                    xlabel('\itf\rm_1'); ylabel('\itf\rm_2');
+                    xlabel('\it f\rm_1'); ylabel('\it f\rm_2');
                 elseif M == 3
                     view(135,30);
-                    xlabel('\itf\rm_1'); ylabel('\itf\rm_2'); zlabel('\itf\rm_3');
+                    xlabel('\it f\rm_1'); ylabel('\it f\rm_2'); zlabel('\it f\rm_3');
                 elseif M > 3
                     view(0,90);
                     xlabel('Dimension No.'); ylabel('Value');

PlatEMO/GUI/Controls/newButtonT.m

@@ -37,7 +37,7 @@
             end
             obj@newButton(parent,pos,str,varargin{:});
             obj.icon = icon;
-            tiplabel = uicontrol('Parent',obj.figure.handle,'Style','text','Units','pixels','Position',[0 0 1 1],'FontSize',10,'String',tip,'BackgroundColor',[1 1 .9]);
+            tiplabel = uicontrol('Parent',obj.figure.handle,'Style','text','Units','pixels','Position',[0 0 1 1],'FontSize',9,'String',tip,'BackgroundColor',[1 1 .9]);
             obj.tip  = uipanel('Parent',obj.figure.handle,'Units','pixels','Position',[tiplabel.Extent(1:3),16],'HighlightColor',[.5 .5 .5],'BorderType','line','Visible','off');
             set(tiplabel,'Parent',obj.tip,'Position',[0 3 tiplabel.Extent(3) 13]);
             obj.updateCData();

PlatEMO/GUI/Modules/GUI.m

@@ -28,7 +28,7 @@
             % Read the function lists
             obj.readList();
             % Create the figure
-            obj.figure = newFigure([0 0 1200 650],'PlatEMO v2.0');
+            obj.figure = newFigure([0 0 1200 650],'PlatEMO v2.1');
             % Create the menu
             obj.menu = newTab(obj.figure,[0 551 1202 100,1 1 0 1],{'Modules','Help'});
             obj.figure.busy = false;

PlatEMO/GUI/Modules/module_experiment.m

@@ -67,7 +67,7 @@
             obj.control.showButton     = newPopmenu3(obj.control.tableToolBar,[681 1 105 25,0 1 1 0],[{obj.GUI.icons.runTimes,[],obj.GUI.icons.runtime,[]},cell(1,length(obj.GUI.metList{1,2})+length(obj.GUI.metList{2,2}))],...
                                                      [{'Run times','','runtime',''},obj.GUI.metList{2,2}',obj.GUI.metList{1,2}'],'callback',@(~,~)obj.flushTable);
             obj.control.table          = newTable(panel,[24 90 788 390,1 1 1 1],'Fontname','Times New Roman','Fontsize',9,'ButtonDownFcn',@obj.cb_table);
-            obj.control.tableMenu      = newMenu(panel,[1 1 245 20]);
+            obj.control.tableMenu      = newMenu(panel,[1 1 250 20]);
             obj.control.tableMenu.add(obj.GUI.icons.showPF,'PF with median metric value','callback',@(~,~)obj.cb_tableMenu(1));
             obj.control.tableMenu.add([],'PF with best metric value','callback',@(~,~)obj.cb_tableMenu(2));
             obj.control.tableMenu.add([],'');

PlatEMO/GUI/Modules/module_test.m

@@ -50,7 +50,7 @@
             obj.control.figureMenu = newMenu(panel,[1 1 155 20]);
             obj.control.figureMenu.add([],'Show in new figure','callback',@obj.cb_figureMenu);
             newTip(obj.control.axesToolBar,[1 1 12 25,1 0 1 0],x,'callback',@obj.cb_figureMenuButton);
-            obj.control.showButton  = newPopmenu3(obj.control.axesToolBar,[453 1 105 25,0 1 1 0],[{obj.GUI.icons.showPF,obj.GUI.icons.showPS,[],obj.GUI.icons.showTPF,[]},cell(1,length(obj.GUI.metList{1,2})+length(obj.GUI.metList{2,2}))],...
+            obj.control.showButton  = newPopmenu3(obj.control.axesToolBar,[443 1 115 25,0 1 1 0],[{obj.GUI.icons.showPF,obj.GUI.icons.showPS,[],obj.GUI.icons.showTPF,[]},cell(1,length(obj.GUI.metList{1,2})+length(obj.GUI.metList{2,2}))],...
                                                   [{'Pareto Front','Pareto Set','','True PF',''},obj.GUI.metList{2,2}',obj.GUI.metList{1,2}'],'callback',@obj.cb_showbutton);
             obj.control.axes        = newAxes(panel,[82 108 452 360,1 1 1 1]);
             obj.control.playbar     = newPlay(panel,[20 32 570 27,1 1 1 0],'callback',@obj.cb_playbar);

PlatEMO/Metrics/CPF.m

@@ -1,6 +1,6 @@
 function Score = CPF(PopObj,PF)
 % <metric> <max>
-% Coverage of Pareto front
+% Coverage over Pareto front
 
 %------------------------------- Reference --------------------------------
 % Y. Tian, R. Cheng, X. Zhang, M. Li, and Y. Jin, Diversity assessment of

PlatEMO/Problems/Sparse MOPs/SMOP1.m

@@ -0,0 +1,57 @@
+classdef SMOP1 < PROBLEM
+% <problem> <Sparse MOP>
+% Benchmark MOP with sparse Pareto optimal solutions
+% theta --- 0.1 --- Sparsity of the Pareto set
+
+%------------------------------- Reference --------------------------------
+% Y. Tian, X. Zhang, C. Wang, and Y. Jin, An evolutionary algorithm for
+% large-scale sparse multi-objective optimization problems, IEEE
+% Transactions on Evolutionary Computation, 2019.
+%------------------------------- Copyright --------------------------------
+% Copyright (c) 2018-2019 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".
+%--------------------------------------------------------------------------
+
+    properties(Access = private)
+        theta = 0.1;    % Sparsity of the Pareto set
+    end
+    methods
+        %% Initialization
+        function obj = SMOP1()
+            obj.theta = obj.Global.ParameterSet(0.1);
+            if isempty(obj.Global.M)
+                obj.Global.M = 2;
+            end
+            if isempty(obj.Global.D)
+                obj.Global.D = 100;
+            end
+            obj.Global.lower    = [zeros(1,obj.Global.M-1)+0,zeros(1,obj.Global.D-obj.Global.M+1)-1];
+            obj.Global.upper    = [zeros(1,obj.Global.M-1)+1,zeros(1,obj.Global.D-obj.Global.M+1)+2];
+            obj.Global.encoding = 'real';
+        end
+        %% Calculate objective values
+        function PopObj = CalObj(obj,X)
+            [N,D] = size(X);
+            M = obj.Global.M;
+            K = ceil(obj.theta*(D-M+1));
+            g = sum(g1(X(:,M:M+K-1),pi/3),2) + sum(g2(X(:,M+K:end),0),2);
+            PopObj = repmat(1+g/(D-M+1),1,M).*fliplr(cumprod([ones(N,1),X(:,1:M-1)],2)).*[ones(N,1),1-X(:,M-1:-1:1)];
+        end
+        %% Sample reference points on Pareto front
+        function P = PF(obj,N)
+            P = UniformPoint(N,obj.Global.M);
+        end
+    end
+end
+
+function g = g1(x,t)
+    g = (x-t).^2;
+end
+
+function g = g2(x,t)
+    g = 2*(x-t).^2 + sin(2*pi*(x-t)).^2;
+end

PlatEMO/Problems/Sparse MOPs/SMOP2.m

@@ -0,0 +1,57 @@
+classdef SMOP2 < PROBLEM
+% <problem> <Sparse MOP>
+% Benchmark MOP with sparse Pareto optimal solutions
+% theta --- 0.1 --- Sparsity of the Pareto set
+
+%------------------------------- Reference --------------------------------
+% Y. Tian, X. Zhang, C. Wang, and Y. Jin, An evolutionary algorithm for
+% large-scale sparse multi-objective optimization problems, IEEE
+% Transactions on Evolutionary Computation, 2019.
+%------------------------------- Copyright --------------------------------
+% Copyright (c) 2018-2019 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".
+%--------------------------------------------------------------------------
+
+    properties(Access = private)
+        theta = 0.1;    % Sparsity of the Pareto set
+    end
+    methods
+        %% Initialization
+        function obj = SMOP2()
+            obj.theta = obj.Global.ParameterSet(0.1);
+            if isempty(obj.Global.M)
+                obj.Global.M = 2;
+            end
+            if isempty(obj.Global.D)
+                obj.Global.D = 100;
+            end
+            obj.Global.lower    = [zeros(1,obj.Global.M-1)+0,zeros(1,obj.Global.D-obj.Global.M+1)-1];
+            obj.Global.upper    = [zeros(1,obj.Global.M-1)+1,zeros(1,obj.Global.D-obj.Global.M+1)+2];
+            obj.Global.encoding = 'real';
+        end
+        %% Calculate objective values
+        function PopObj = CalObj(obj,X)
+            [N,D] = size(X);
+            M = obj.Global.M;
+            K = ceil(obj.theta*(D-M+1));
+            g = sum(g2(X(:,M:M+K-1),pi/3),2) + sum(g3(X(:,M+K:end),0),2);
+            PopObj = repmat(1+g/(D-M+1),1,M).*fliplr(cumprod([ones(N,1),X(:,1:M-1)],2)).*[ones(N,1),1-X(:,M-1:-1:1)];
+        end
+        %% Sample reference points on Pareto front
+        function P = PF(obj,N)
+            P = UniformPoint(N,obj.Global.M);
+        end
+    end
+end
+
+function g = g2(x,t)
+    g = 2*(x-t).^2 + sin(2*pi*(x-t)).^2;
+end
+
+function g = g3(x,t)
+    g = 4-(x-t)-4./exp(100*(x-t).^2);
+end