Life skills are the basic abilities we need to handle daily challenges and live a healthy, balanced life. They help us think clearly, manage our feelings, make good decisions, solve problems, and build good relationships with others. The World Health Organization (WHO) highlights 10 important life skills: 1.) Thinking skills: decision-making, problem-solving, creative thinking, critical thinking 2.) Social skills: communication, empathy, interpersonal skills 3.) Emotional skills: self-awareness, coping with emotions, coping with stress Life skills are the tools that make us stronger, wiser, and calmer in real life — at home, in school, at work, and in the community :) Life Skills for Everyday Success ~xRay Pixy https://youtu.be/AMsUfKRl4kw Video Chapters: Life Skills 00:00 Introduction 01:07 Life Skills 09:42 Real Life Challenge 13:44 Task For You #LifeSkills #SuccessTools #StressFreeLiving #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swa...
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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