Merge pull request #61 from DestinyMy/master

Add four new algorithms and fix some minor bugs

Doc/releasenote.md

@@ -1,4 +1,10 @@
+# Release Highlights of PlatEMO 2.8  
+
+* Add three algorithms for constrained optimization (i.e., CCMO, MOEA/D-DAE, and TiGE-2) and an algorithm for many-objective optimization (i.e., PREA). There are currently 118 algorithms in the platform.
+* Fix some minor bugs in the Pareto front sampling methods in LIR-CMOP and MW problems.
+
 # Release Highlights of PlatEMO 2.6  
+
 * Add two algorithms: MOEA/PSL and DWU. There are currently 112 algorithms in the platform.
 
 # Release Highlights of PlatEMO 2.5  
@@ -29,4 +35,4 @@
 # Release Highlights of PlatEMO 2.0
 * __Lighter framework.__ The architecture of PlatEMO is simplified, which leads to lower learning cost and higher efficiency. The result file size is also reduced.  
 * __Higher efficiency.__ The runtime of Pareto dominance based algorithms is reduced by using a more efficient non-dominated sorting algorithm. The runtime of decomposition based algorithms is reduced due to the new architecture of PlatEMO. The runtime of hypervolume calculation is reduced by new logic and GPU acceleration. In experimental module, the algorithms can be executed in parallel.  
-* __More conveniences.__ The populations obtained during the evolutionary process can be saved in result files. The references of each algorithm, problem, operator, and metric are given in the comments of the function. The codes of GUI are now open source.
+* __More conveniences.__ The populations obtained during the evolutionary process can be saved in result files. The references of each algorithm, problem, operator, and metric are given in the comments of the function. The codes of GUI are now open source.

PlatEMO/Algorithms/CCMO/CCMO.m

@@ -0,0 +1,44 @@
+function CCMO(Global)
+% <algorithm> <C>
+% Coevolutionary constrained multi-objective optimization framework
+% type --- 1 --- Type of operator (1. GA 2. DE)
+
+%------------------------------- Reference --------------------------------
+% Y. Tian, T. Zhang, J. Xiao, X. Zhang, and Y. Jin, A coevolutionary
+% framework for constrained multi-objective optimization problems, IEEE
+% Transactions on Evolutionary Computation, 2020.
+%------------------------------- 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
+    type = Global.ParameterSet(1);
+
+    %% Generate random population
+    Population1 = Global.Initialization();
+    Population2 = Global.Initialization();
+    Fitness1    = CalFitness(Population1.objs,Population1.cons);
+    Fitness2    = CalFitness(Population2.objs);
+
+    %% Optimization
+    while Global.NotTermination(Population1)
+        if type == 1
+            MatingPool1 = TournamentSelection(2,Global.N,Fitness1);
+            MatingPool2 = TournamentSelection(2,Global.N,Fitness2);
+            Offspring1  = GAhalf(Population1(MatingPool1));
+            Offspring2  = GAhalf(Population2(MatingPool2));
+        elseif type == 2
+            MatingPool1 = TournamentSelection(2,2*Global.N,Fitness1);
+            MatingPool2 = TournamentSelection(2,2*Global.N,Fitness2);
+            Offspring1  = DE(Population1,Population1(MatingPool1(1:end/2)),Population1(MatingPool1(end/2+1:end)));
+            Offspring2  = DE(Population2,Population2(MatingPool2(1:end/2)),Population2(MatingPool2(end/2+1:end)));
+        end
+        [Population1,Fitness1] = EnvironmentalSelection([Population1,Offspring1,Offspring2],Global.N,true);
+        [Population2,Fitness2] = EnvironmentalSelection([Population2,Offspring1,Offspring2],Global.N,false);
+    end
+end

PlatEMO/Algorithms/CCMO/CalFitness.m

@@ -0,0 +1,56 @@
+function Fitness = CalFitness(PopObj,PopCon)
+% Calculate the fitness of each solution
+
+%------------------------------- 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".
+%--------------------------------------------------------------------------
+
+    N = size(PopObj,1);
+    if nargin == 1
+        CV = zeros(N,1);
+    else
+        CV = sum(max(0,PopCon),2);
+    end
+
+    %% Detect the dominance relation between each two solutions
+    Dominate = false(N);
+    for i = 1 : N-1
+        for j = i+1 : N
+            if CV(i) < CV(j)
+                Dominate(i,j) = true;
+            elseif CV(i) > CV(j)
+                Dominate(j,i) = true;
+            else
+                k = any(PopObj(i,:)<PopObj(j,:)) - any(PopObj(i,:)>PopObj(j,:));
+                if k == 1
+                    Dominate(i,j) = true;
+                elseif k == -1
+                    Dominate(j,i) = true;
+                end
+            end
+        end
+    end
+
+    %% Calculate S(i)
+    S = sum(Dominate,2);
+
+    %% Calculate R(i)
+    R = zeros(1,N);
+    for i = 1 : N
+        R(i) = sum(S(Dominate(:,i)));
+    end
+
+    %% Calculate D(i)
+    Distance = pdist2(PopObj,PopObj);
+    Distance(logical(eye(length(Distance)))) = inf;
+    Distance = sort(Distance,2);
+    D = 1./(Distance(:,floor(sqrt(N)))+2);
+
+    %% Calculate the fitnesses
+    Fitness = R + D';
+end

PlatEMO/Algorithms/CCMO/EnvironmentalSelection.m

@@ -0,0 +1,51 @@
+function [Population,Fitness] = EnvironmentalSelection(Population,N,isOrigin)
+% The environmental selection of SPEA2
+
+%------------------------------- 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".
+%--------------------------------------------------------------------------
+
+    %% Calculate the fitness of each solution
+    if isOrigin
+        Fitness = CalFitness(Population.objs,Population.cons);
+    else
+        Fitness = CalFitness(Population.objs);
+    end
+
+    %% Environmental selection
+    Next = Fitness < 1;
+    if sum(Next) < N
+        [~,Rank] = sort(Fitness);
+        Next(Rank(1:N)) = true;
+    elseif sum(Next) > N
+        Del  = Truncation(Population(Next).objs,sum(Next)-N);
+        Temp = find(Next);
+        Next(Temp(Del)) = false;
+    end
+    % Population for next generation
+    Population = Population(Next);
+    Fitness    = Fitness(Next);
+    % Sort the population
+    [Fitness,rank] = sort(Fitness);
+    Population = Population(rank);
+end
+
+function Del = Truncation(PopObj,K)
+% Select part of the solutions by truncation
+
+    %% Truncation
+    Distance = pdist2(PopObj,PopObj);
+    Distance(logical(eye(length(Distance)))) = inf;
+    Del = false(1,size(PopObj,1));
+    while sum(Del) < K
+        Remain   = find(~Del);
+        Temp     = sort(Distance(Remain,Remain),2);
+        [~,Rank] = sortrows(Temp);
+        Del(Remain(Rank(1))) = true;
+    end
+end

PlatEMO/Algorithms/EIM-EGO/InfillSamplingEIM.m

@@ -38,10 +38,10 @@
         end
         s = sqrt(max(0,mse));
         % the EI matrix (three dimensional matrix)
-        f_matrix  =  f .* ones(1,1,GAPopulationSize);
-        u_matrix = reshape(u', 1, M, GAPopulationSize).* ones(p,1);
-        s_matrix =  reshape(s', 1, M, GAPopulationSize).* ones(p,1);
-        EI_matrix=(f_matrix-u_matrix).*Gaussian_CDF((f_matrix-u_matrix)./s_matrix)+s_matrix.*Gaussian_PDF((f_matrix-u_matrix)./s_matrix);
+        f_matrix  = f .* ones(1,1,GAPopulationSize);
+        u_matrix  = reshape(u', 1, M, GAPopulationSize).* ones(p,1);
+        s_matrix  = reshape(s', 1, M, GAPopulationSize).* ones(p,1);
+        EI_matrix = (f_matrix-u_matrix).*Gaussian_CDF((f_matrix-u_matrix)./s_matrix)+s_matrix.*Gaussian_PDF((f_matrix-u_matrix)./s_matrix);
         switch InfillCriterionIndex
             case 1
                 %  the Euclidean distance-based EI matrix criterion

PlatEMO/Algorithms/LSMOF/WeightOptimization.m

@@ -14,14 +14,14 @@
 % This function is written by Cheng He
 
 	%% Choose NF solutions as the reference solutions
-	Reference   = max(Population.objs,[],1);
+	Reference    = max(Population.objs,[],1);
     [RefPop,~,~] = EnvironmentalSelection(Population,wD);
 
     %% Calculate the reference directions
-	lower       = Global.lower;upper = Global.upper;
-	Direction   = [sum((RefPop.decs-repmat(lower,wD,1)).^2,2).^(0.5);sum((repmat(upper,wD,1)-RefPop.decs).^2,2).^(0.5)];
-	Direct      = [(RefPop.decs-repmat(lower,wD,1));(repmat(upper,wD,1)-RefPop.decs)]./repmat(Direction,1,Global.D);
-	wmax        = sum((upper-lower).^2)^(0.5)*0.5;
+	lower     = Global.lower;upper = Global.upper;
+	Direction = [sum((RefPop.decs-repmat(lower,wD,1)).^2,2).^(0.5);sum((repmat(upper,wD,1)-RefPop.decs).^2,2).^(0.5)];
+	Direct    = [(RefPop.decs-repmat(lower,wD,1));(repmat(upper,wD,1)-RefPop.decs)]./repmat(Direction,1,Global.D);
+	wmax      = sum((upper-lower).^2)^(0.5)*0.5;
 
     %% Optimize the weight variables by DE
 	w0 = rand(N,2*wD).*wmax;                                % Initialize the population
@@ -85,7 +85,7 @@
         PopDec  = [repmat(w0(i,1:WD)',1,Global.D).*direct(1:WD,:)+repmat(Global.lower,WD,1);
                       repmat(Global.upper,WD,1) - repmat(w0(i,WD+1:end)',1,Global.D).*direct(WD+1:end,:)];
         OffWPop = INDIVIDUAL(PopDec);
-        OffSpring  = [OffSpring,OffWPop];
+        OffSpring = [OffSpring,OffWPop];
         Obj(i)  = -HV(OffWPop.objs,Reference);
     end
 end

PlatEMO/Algorithms/MOEA-D-DAE/ArchiveUpdate.m

@@ -0,0 +1,74 @@
+function Population = ArchiveUpdate(Population,N)
+% Update archive
+
+%------------------------------- 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".
+%--------------------------------------------------------------------------
+
+    %% Select feasible solutions
+    fIndex     = all(Population.cons <= 0,2);
+    Population = Population(fIndex);
+    if isempty(Population)
+        return
+    else
+        if size(Population.objs,2)==2
+            %% Non-dominated sorting
+            [FrontNo,~] = NDSort(Population.objs,1);
+            Next = (FrontNo == 1);    
+            Population = Population(Next);    
+            if sum(Next) > N
+                %% Calculate the crowding distance of each solution
+                CrowdDis   = CrowdingDistance(Population.objs);
+                [~,Rank]   = sort(CrowdDis,'descend');
+                Population = Population(Rank(1:N));
+            end
+        else    
+            Population = Population(NDSort(Population.objs,1)==1);
+            Population = Population(randperm(length(Population)));
+            PCObj = Population.objs;
+            nND   = length(Population);
+            %% Population maintenance
+            if length(Population) > N
+                % Normalization
+                fmax  = max(PCObj,[],1);
+                fmin  = min(PCObj,[],1);
+                PCObj = (PCObj-repmat(fmin,nND,1))./repmat(fmax-fmin,nND,1);
+                % Determine the radius of the niche
+                d  = pdist2(PCObj,PCObj);
+                d(logical(eye(length(d)))) = inf;
+                sd = sort(d,2);
+                r  = mean(sd(:,min(3,size(sd,2))));
+                R  = min(d./r,1);
+                % Delete solution one by one
+                while length(Population) > N
+                    [~,worst]  = max(1-prod(R,2));
+                    Population(worst)  = [];
+                    R(worst,:) = [];
+                    R(:,worst) = [];
+                end
+            end
+        end
+    end
+end
+
+function CrowdDis = CrowdingDistance(PopObj)
+% Calculate the crowding distance of each solution
+
+    [N,M]    = size(PopObj);
+    CrowdDis = zeros(1,N);
+    Fmax     = max(PopObj,[],1);
+    Fmin     = min(PopObj,[],1);
+    for i = 1 : M
+        [~,Rank] = sortrows(PopObj(:,i));
+        CrowdDis(Rank(1))   = inf;
+        CrowdDis(Rank(end)) = inf;
+        for j = 2 : (N - 1)
+            CrowdDis(Rank(j)) = CrowdDis(Rank(j))+(PopObj(Rank(j+1),i)-PopObj(Rank(j-1),i))/(Fmax(i)-Fmin(i));
+        end
+    end
+end