Suppress warning & Cherry pick Merge PR #130

PlatEMO/Algorithms/Multi-objective optimization/I-SMOA/ISMOA.m

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+classdef ISMOA < ALGORITHM
+% <multi> <real> <expensive>
+% Iterative supervised multi-objective optimization algorithm
+% H --- 2.6e4 --- Size of uniformly distributed L1 unit vector set
+
+%------------------------------- Reference --------------------------------
+% T. Takagi, K. Takadama, and H. Sato, Pareto front upconvert by iterative
+% estimation modeling and solution sampling, Proceedings of The 12th
+% Edition of International Conference Series on Evolutionary Multi-
+% Criterion Optimization, Lecture Notes in Computer Science, 2023, 13970:
+% 218–230.
+%------------------------------- Copyright --------------------------------
+% Copyright (c) 2023 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".
+%--------------------------------------------------------------------------
+
+% This function is written by Tomoaki Takagi
+
+% This function requires solution data like 'DTLZ2_M3_D12_ILD.mat' or
+% 'DTLZ2_M3_D12_ILD.dat'. This function return all evaluated solutions.
+% This function is deterministic method without random numbers.
+
+    methods
+        function main(Algorithm,Problem)
+            %% Parameter setting
+            H = Algorithm.ParameterSet(2.6e4);
+
+            %% Generate the uniformly distributed L1 unit vector set
+            [W,N] = UniformPoint(H,Problem.M,'ILD');
+            W(W==1e-6) = 0;
+
+            %% Generate estimator
+            if ~isempty(ver('nnet'))
+                estimator = @(W,X,Y) sim(newrbe(X',Y'),W')';
+            else
+                I = ones(1,M);
+                estimator = @(W,X,Y) predictor(W, ...
+                    dacefit(X,Y,'regpoly0','corrgauss',I,1e-3*I,1e3*I));
+            end
+
+            %% Load solution data
+            Population = loadData(Problem);
+
+            %%  Iterative loop
+            while size(Population) < Problem.maxFE
+                Obj = Population.objs;
+                Dec = Population.decs;
+
+                % Generate model values
+                Y = vecnorm(Obj,1,2); % L1 norm set
+                X = Obj./Y;           % L1 unit vector set
+
+                % Single solution sampling
+                objs = W.*estimator(W,X,Y);
+                [~,index] = max(min(pdist2(objs,Obj),[],2));
+
+                % Evaluation and upconvert
+                dec = zeros(1,Problem.D);
+                for i = 1 : Problem.D
+                    dec(i) = estimator(W(index,:),X,Dec(:,i));
+                end
+                dec = min(max(dec,Problem.lower),Problem.upper);
+                Population = [Population,Problem.Evaluation(dec)];
+            end
+            Algorithm.NotTerminated(Population);
+        end
+    end
+end