Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

Whale Optimization Algorithm Code Implementation

Whale Optimization Algorithm Code Files

function obj_fun(test_fun)

switch test_fun

    case 'F1'

        x = -100:2:100; y=x;

    case 'F2'

        x = -10:2:10; y=x;

end

end

function [LB,UB,D,FitFun]=test_fun_info(C)

switch C

    case 'F1'

        FitFun = @F1;

        LB = -100; 

        UB = 100;

        D = 30;

    case 'F2'

        FitFun = @F2;

        LB = -10;

        UB = 10;

        D = 30;

end

% F1 Test Function

    function r = F1(x)

        r = sum(x.^2);

    end

% F2 Test Function

    function r = F2(x)

        r = sum(abs(x))+prod(abs(x));

    end

end

function Position = initialize(Pop_Size,D,UB,LB)

SS_Bounds = size(UB,2);

if SS_Bounds == 1

    Position = rand(Pop_Size,D).*(UB-LB)+LB;

end

if SS_Bounds>1

    for i = 1:D

        UB_i = UB(i);

        LB_i = LB(i);

        Position(:,i) = rand(Pop_Size,1).*(UB_i-LB_i)+LB_i; 

    end

end

end

function [Best_Val,Best_Pos,Convergence_Curve]=WOA(Pop_Size,MaxT,LB,UB,D,FitFun)

Best_Pos = zeros(1,D);

Best_Val = inf;

Position = initialize(Pop_Size,D,UB,LB);

Convergence_Curve = zeros(1,MaxT);

T = 0;

while T<MaxT

    for i = 1:size(Position,1)

        CheckUB = Position(i,:)>UB;

        CheckLB = Position(i,:)<LB;

        Position(i,:) = (Position(i,:).*(~(CheckUB+CheckLB)))+UB.*CheckUB+LB.*CheckLB;

        %Calculate Fitness Values

        Fitness_Val = FitFun(Position(i,:));

        %Compare Fitness Values

        if Fitness_Val<Best_Val

            Best_Val = Fitness_Val;

            Best_Pos = Position(i,:);

        end

    end

    a = 2-T*((2)/MaxT);

    a2 = -1+T*((-1)/MaxT);

    

    %Agents Position Update (New Positions)

    for i=1:size(Position,1)

        r1=rand();

        r2=rand();

        A = 2*a*r1-a;

        C = 2 * r2;

        b = 1;

        l = (a2-1)*rand+1;

        p = rand();

        for j = 1:size(Position,2)

            if p<0.5

                if abs(A)>1

                    rand_best_index=floor(Pop_Size*rand()+1);

                    X_rand = Position(rand_best_index,:);

                    D_X_rand = abs(C*X_rand(j)-Position(i,j));

                    Position(i,j) = X_rand(j)-A*D_X_rand;

                elseif abs(A)<1

                    D_Best = abs(C*Best_Pos(j)-Position(i,j));

                    Position(i,j) = Best_Pos(j)-A*D_Best;

                end

            elseif p>=0.5

                distance2Best = abs(Best_Pos(j)-Position(i,j));

                Position(i,j) = distance2Best * exp(b.*1).*cos(1.*2*pi)+Best_Pos(j);    

            end

        end  

    end

    T = T + 1;  %Counter Increment

    Convergence_Curve(T) = Best_Val;

    [T Best_Val]

end

end

clc

clear all

Pop_Size = 100;

Objective_Fun = 'F2';

MaxT = 500;

[LB,UB,D,FitFun] = test_fun_info(Objective_Fun);

[Best_Val,Best_Pos,Sol_Convergence]=WOA(Pop_Size,MaxT,LB,UB,D,FitFun);

subplot(1,1,1);

semilogy(Sol_Convergence,'Color','r');

title('Convergence Curve');

xlabel('Iteration');

ylabel('Best Value');

axis tight

grid on

box on

legend ('WOA')

display(['Best Position',num2str(Best_Pos)]);

display(['Best_Value ',num2str(Best_Val)]);