The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā Strong branches grow ...
Learn Pelican Optimization Algorithm Code Implementation Step-By-StepPOA-CODE Video Chapters:00:00 Introduction01:22 Test Function Information Program File02:37 Pelican Optimization Algorithm Program File11:23 Main Program File12:30 Conclusion
1.) Test Function Information File
function [LB,UB,D,FitF] = test_fun_info(C)
switch C
case 'F1'
FitF = @F1;
LB=-100;
UB =100;
D =30;
case 'F2'
FitF = @F2;
LB=-10;
UB =10;
D =30;
case 'F3'
FitF = @F3;
LB=0;
UB=1;
D=3;
end
end
% F1
function R = F1(x)
R=sum(x.^2);
end
% F2
function R = F2(x)
R=sum(abs(x))+prod(abs(x));
end
2.) POA File
function[Best_Solution,Best_Location,Sol_con_Curve]=POA(PopSize,MaxT,LB,UB,D,FitF)
LB=ones(1,D).*(LB); % Lower limit
UB=ones(1,D).*(UB); % Upper limit
% POPULATION INITIALIZATION PHASE
for i=1:D
X(:,i) = LB(i)+rand(PopSize,1).*(UB(i) - LB(i)); % Initial population
end
% FITNESS VALUES CALCULATION
for i =1:PopSize
L=X(i,:);
FitnessVal(i)=FitF(L);
end
%%
for t=1:MaxT
%% update the best condidate solution
[Best_Agent_Val , Best_Agent_Loc]=min(FitnessVal);
if t==1
Best_Pos=X(Best_Agent_Loc,:); % Optimal location
Best_Val=Best_Agent_Val; % The optimization objective function
elseif Best_Agent_Val<Best_Val
Best_Val=Best_Agent_Val;
Best_Pos=X(Best_Agent_Loc,:);
end
%% UPDATE location of food
Agents_Target=[];
g=randperm(PopSize,1);
Agents_Target=X(g,:);
Agents_Target=FitnessVal(g);
%%
for i=1:PopSize
%% PHASE 1: Moving towards prey (exploration phase)
I=round(1+rand(1,1));
if FitnessVal(i)> Agents_Target
New_Pos=X(i,:)+ rand(1,1).*(Agents_Target-I.* X(i,:)); %Eq(4)
else
New_Pos=X(i,:)+ rand(1,1).*(X(i,:)-1.*Agents_Target); %Eq(4)
end
New_Pos= max(New_Pos,LB);
New_Pos = min(New_Pos,UB);
% Updating X_i using (5)
New_Fit = FitF(New_Pos);
if New_Fit <= FitnessVal(i)
X(i,:) = New_Pos;
FitnessVal(i)=New_Fit;
end
%% END PHASE 1: Moving towards prey (exploration phase)
%% PHASE 2: Winging on the water surface (exploitation phase)
New_Pos=X(i,:)+0.2*(1-t/MaxT).*(2*rand(1,D)-1).*X(i,:);% Eq(6)
New_Pos= max(New_Pos,LB);
New_Pos = min(New_Pos,UB);
% Updating X_i using (7)
New_Fit = FitF(New_Pos);
if New_Fit <= FitnessVal(i)
X(i,:) = New_Pos;
FitnessVal(i)=New_Fit;
end
%% END PHASE 2: Winging on the water surface (exploitation phase)
end
best_so_far(t)=Best_Val;
average(t) = mean (FitnessVal);
end
Best_Solution=Best_Val;
Best_Location=Best_Pos;
Sol_con_Curve=best_so_far;
end
3.) Main File
clc
clear all
%Test Function
Test_Fun='F3';
% Total Number of Pelicans
PopSize=50;
% Maximum number of iteration
MaxT=500;
% Test Function Details
[LB,UB,D,FitF]=test_fun_info(Test_Fun);
% POA Calculation
[BestVal,BestLoc,Sol_con_Curve]=POA(PopSize,MaxT,LB,UB,D,FitF);
subplot(1,1,1);
semilogy(Sol_con_Curve,'Color','r');
title('Convergence Curve');
xlabel('Iteration');
ylabel('Best Value');
axis tight
grid on
box on
legend ('POA')
% Display Solution
display(['Best Position' [num2str(Test_Fun)],' = ', num2str(BestLoc)]);
display(['Best Solution' [num2str(Test_Fun)],' = ', num2str(BestVal)]);
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