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53 changes: 53 additions & 0 deletions
53
PlatEMO/Algorithms/Multi-objective optimization/MCCMO/CalFitness1.m
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| function Fitness = CalFitness1(PopObj,PopCon,processcon,epsilon) | ||
| % Calculate the fitness of each solution | ||
|
|
||
| %------------------------------- 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". | ||
| %-------------------------------------------------------------------------- | ||
|
|
||
| N = size(PopObj,1); | ||
| CV = PopCon(:,processcon); | ||
| CV = sum(max(0,CV),2); | ||
| CV(find(CV<=epsilon)) = 0; | ||
| %% 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); | ||
|
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||
| %% 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); | ||
|
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||
| %% Calculate the fitnesses | ||
| Fitness = R + D'; | ||
| end |
56 changes: 56 additions & 0 deletions
56
PlatEMO/Algorithms/Multi-objective optimization/MGSAEA/CalFitness.m
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| function Fitness = CalFitness(PopObj,PopCon) | ||
| % Calculate the fitness of each solution | ||
|
|
||
| %------------------------------- 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". | ||
| %-------------------------------------------------------------------------- | ||
|
|
||
| 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); | ||
|
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||
| %% Calculate the fitnesses | ||
| Fitness = R + D'; | ||
| end |
81 changes: 81 additions & 0 deletions
81
PlatEMO/Algorithms/Multi-objective optimization/MGSAEA/EnvironmentalSelection.m
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,81 @@ | ||
| function [PopDec,PopObj,Fitness] = EnvironmentalSelection(PopDec,PopObj,NI,M,status) | ||
| % Environmental selection | ||
|
|
||
| %------------------------------- 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". | ||
| %-------------------------------------------------------------------------- | ||
|
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||
| %% Delete the duplicated points | ||
| [~, Unduplicated] = unique(PopObj(:,1:M),'rows'); | ||
| PopDec = PopDec(Unduplicated,:); | ||
| PopObj = PopObj(Unduplicated,:); | ||
|
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||
| %% Calculate the fitness of each solution | ||
| if nargin == 4 | ||
| Fitness = CalFitness(PopObj); | ||
| else | ||
| if status == 1 | ||
| RealObj = PopObj(:,1:M); | ||
| CV = PopObj(:,end); | ||
| Fitness = CalFitness(RealObj,max(0,CV)); | ||
| elseif status == 2 | ||
| RealObj = PopObj(:,1:M); | ||
| PopCon = PopObj(:,M+1:end); | ||
| CV = sum(max(0,PopCon),2); | ||
| Fitness = CalFitness([RealObj,CV]); | ||
| elseif status == 3 | ||
| Fitness = CalFitness(PopObj); | ||
| end | ||
| end | ||
|
|
||
| %% Environmental selection | ||
| if nargin == 4 | ||
| Next = Fitness < 1; | ||
| if sum(Next) < NI | ||
| [~,Rank] = sort(Fitness); | ||
| Next(Rank(1:NI)) = true; | ||
| elseif sum(Next) > NI | ||
| Del = Truncation(PopObj(Next,:),sum(Next)-NI); | ||
| Temp = find(Next); | ||
| Next(Temp(Del)) = false; | ||
| end | ||
| else | ||
| Next = Fitness < 1; | ||
| if sum(Next) < NI | ||
| [~,Rank] = sort(Fitness); | ||
| Next(Rank(1:NI)) = true; | ||
| elseif sum(Next) > NI | ||
| if status ~=3 | ||
| RealObj = PopObj(:,1:M); | ||
| else | ||
| RealObj = PopObj; | ||
| end | ||
| Del = Truncation(RealObj(Next,:),sum(Next)-NI); | ||
| Temp = find(Next); | ||
| Next(Temp(Del)) = false; | ||
| end | ||
| end | ||
| PopDec = PopDec(Next,:); | ||
| PopObj = PopObj(Next,:); | ||
| Fitness = Fitness(Next); | ||
| end | ||
|
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| function Del = Truncation(PopObj,K) | ||
| % Select part of the solutions by truncation | ||
|
|
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| %% 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 |
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