Why the given codes give errors for vector bounds but runs well for scalar bounds?

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Sadiq Akbar
Sadiq Akbar 2023-7-31
评论： Sadiq Akbar 2023-8-1
I have downloaded an algorithm BBO from the Mathworks site. I run it with my fitness function "fitVectorized1.m". I oberved the following;
1- When I run it for scalar bounds, it works and gives reults
But
2- When I run it for vector bounds, it gives me errors.
What to do?
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KSSV
KSSV 2023-7-31
u=[1 5 30 70];dim=length(u);
[BestX,fmin]=bbo3(@(b)fitVectorized1(b,u),dim,0,90,100,50)
Unrecognized function or variable 'RouletteWheelSelection'.
Error in bbo3 (line 75)
j=RouletteWheelSelection(EP);

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KSSV
KSSV 2023-7-31
Use this function instead of your bbo3.
function [BestX,fmin]=bbo3(obj,dim,lb,ub,iter,pop1)
%% Problem Definition
CostFunction=obj; % Cost Function
nVar=dim; % Number of Decision Variables
VarSize=[1 nVar]; % Decision Variables Matrix Size
VarMin=lb; % Decision Variables Lower Bound
VarMax=ub; % Decision Variables Upper Bound
%% BBO Parameters
MaxIt=iter; % Maximum Number of Iterations
nPop=pop1;%50; % Number of Habitats (Population Size)
KeepRate=0.01;%0.2; % Keep Rate
nKeep=round(KeepRate*nPop); % Number of Kept Habitats
nNew=nPop-nKeep; % Number of New Habitats
% Migration Rates
mu=linspace(1,0,nPop); % Emmigration Rates
lambda=1-mu; % Immigration Rates
alpha=0.9;
pMutation=0.1;
sigma=0.02*(VarMax-VarMin);
%% Initialization
% Empty Habitat
habitat.Position=zeros([],dim);
habitat.Cost=[];
% Create Habitats Array
pop=repmat(habitat,nPop,1);
% Initialize Habitats
for i=1:nPop
pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
pop(i).Cost=CostFunction(pop(i).Position);
end
% Sort Population
[~, SortOrder]=sort([pop.Cost]);
pop=pop(SortOrder);
% Best Solution Ever Found
BestSol=pop(1);
% Array to Hold Best Costs
BestCost=zeros(MaxIt,1);
%% BBO Main Loop
for it=1:MaxIt
newpop=pop;
for i=1:nPop
for k=1:nVar
% Migration
if rand<=lambda(i)
% Emmigration Probabilities
EP=mu;
EP(i)=0;
EP=EP/sum(EP);
% Select Source Habitat
j=RouletteWheelSelection(EP);
% Migration
newpop(i).Position(k)=pop(i).Position(k) ...
+alpha*(pop(j).Position(k)-pop(i).Position(k));
end
% Mutation
if rand<=pMutation
newpop(i).Position(k)=newpop(i).Position(k)+sigma(k)*randn; %<------ Changed here
end
end
% Apply Lower and Upper Bound Limits
newpop(i).Position = max(newpop(i).Position, VarMin);
newpop(i).Position = min(newpop(i).Position, VarMax);
% Evaluation
newpop(i).Cost=CostFunction(newpop(i).Position);
end
% Sort New Population
[~, SortOrder]=sort([newpop.Cost]);
newpop=newpop(SortOrder);
% Select Next Iteration Population
pop=[pop(1:nKeep)
newpop(1:nNew)];
% Sort Population
[~, SortOrder]=sort([pop.Cost]);
pop=pop(SortOrder);
% Update Best Solution Ever Found
BestSol=pop(1);
% Store Best Cost Ever Found
BestCost(it)=BestSol.Cost;
% Show Iteration Information
%disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
end
%% Results
% % figure;
% % %plot(BestCost,'LineWidth',2);
% % semilogy(BestCost,'LineWidth',2);
% % xlabel('Iteration');
% % ylabel('Best Cost');
% % grid on;
BestX=BestSol.Position % By Me
fmin=BestSol.Cost % By Me

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