MATLAB code for Bear Smell Search Algorithm (BSSA)

Bear Smell Search Algorithm 

clc;

clear;

close all;

%% parameter setup

N=100;                  % number of population

Function_name='F1'; % Name of the test function that can be from F1 to   F23 

[lb,ub,dim,Fobj]=Get_Functions_details(Function_name);

maxiter=500;            % maximum iterations

thresh1=1.2;              % threshold 1

thresh2=50;             % threshold 2

% initialize population

bear_odour=initialization(N,dim,lb,ub);     % generate initial random population

bear_fitness=zeros(1,N);                    % Fitness

C1=zeros(N,dim);C2=zeros(N,dim);C3=zeros(N,dim);C4=zeros(N,dim);

POC=zeros(N,dim);

POf=zeros(1,N);

DOC=zeros(N,dim);

EOF=zeros(1,N);

t=0;

Score=inf; 

tic;

%% main loop begins here

while t<maxiter

    for i=1:size(bear_odour,1)

        Flag4Upperbound=bear_odour(i,:)>ub;

        Flag4Lowerbound=bear_odour(i,:)<lb;

        bear_odour(i,:)=(bear_odour(i,:).*(~(Flag4Upperbound+Flag4Lowerbound)))+ub.*Flag4Upperbound+lb.*Flag4Lowerbound;

        bear_fitness(i)=Fobj(bear_odour(i,:));              % evaluate fitness

        if min(bear_fitness)<Score 

            Score=bear_fitness(i); 

            best_odor=bear_odour(i,:);

        end

    end

   for i=1:size(bear_odour,1)

   for j=1:size(bear_odour,2)

        POC(i,j)=bear_odour(i,j)/max(bear_odour(i,:))+rand;         % probability odor component

        POf(i)=bear_fitness(i)/max(bear_fitness);                   % Probability odor fitness

        POF_global=min(POf);                                        % global solution

        DOC(i,j)=1-(sum(POC(i,j)-(POC(i,j).*POC(i,j))/sqrt(sum((POC(i,j)-(POC(i,j).*POC(i,j))))^2)));   % distance odor components

        EOF(i)=abs(POf(i)-POF_global)+rand;                         % expected odor fitness

        C1(i,j)=-EOF(i)*((2-(DOC(i,j)/thresh2)));                   % Coefficient C1

        C2(i,j)=-EOF(i)*((2-(DOC(i,j)/thresh1)));                   % Coefficient C2

        C3(i,j)=EOF(i)*((2-(DOC(i,j)/thresh2)));                    % Coefficient C3

        C4(i,j)=EOF(i)*((2-(DOC(i,j)/thresh1)));                    % Coefficient C4

   end

   end

 for i4=1:N

    if DOC(i4)<= thresh2 && EOF(i4)<=thresh1

        bear_odour(i4,:)=C1(i4)*bear_odour(i4,:)-rand*(C2(i4))*(bear_odour(i4,:)-best_odor);

    else

        bear_odour(i4,:)=C3(i4)*bear_odour(i4,:)-rand*(C4(i4))*(bear_odour(i4,:)-best_odor);

    end

end

    t=t+1;

    Convergence(t)=Score;

    fprintf('Iteration number = %d , Best score is = %d\n',(t),(Score))

end

timec=toc;

%%  plot convergence curve

figure;

semilogy(Convergence,'b','linewidth',2);hold on

xlabel('Number of iterations','fontsize',11,'fontname','Times','fontweight','bold');

ylabel('Fitness','fontsize',11,'fontname','Times','fontweight','bold');

title('Convergence curve','fontsize',11,'fontname','Times','fontweight','bold');

legend('Bear Smell Search Algorithm')

grid on

set(gca,'fontname','times','fontweight','bold','fontsize',11)

fprintf('Time taken (in ms)= %d',(timec))

Refer : Ghasemi-Marzbali, Ali. "A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm." Soft Computing 24.17 (2020): 13003-13035.

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