Learn how to implement an obstacle-avoiding path planning for a robot using the Grey Wolf Optimization (GWO) in a static environment. #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
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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