社交滑雪驾驶员优化算法Social Ski-Driver (SSD) 附matlab代码

%Social Ski-Driver (SSD) optimization algorithm %More details about the algorithm are in [please cite the original paper (below)] %Alaa Tharwat, Thomas Gabel, "Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm" %Neural Computing and Applications, pp. 1-14, 2019 %Tharwat, A. & Gabel, T. Neural Comput & Applic (2019). https://doi.org/10.1007/s00521-019-04159-z clc clear all Max_iterations=50; % Maximum Number of Iterations correction_factor = 2.0; % Correction factor inertia = 1.0; % Ineritia Coeffecient swarm_size = 20; % Number of particles LB=[-5.12 -5.12]; % Lower Boundaries UB=[5.12 5.12]; % Upper Boundaries xrange=UB(1)-LB(1); yrange=UB(2)-LB(2); Dim=size(UB,2); Runs=20; for r=1:Runs % clear swarm swarm(:, 1, 1)=rand(1,swarm_size)*xrange+LB(1); swarm(:, 1, 2)=rand(1,swarm_size)*yrange+LB(2); % Initial best value so far swarm(:, 4, 1) = inf; % Initial velocity swarm(:, 2, :) = 0; for iter = 1 : Max_iterations % Calculating fitness value for all particles for i = 1 : swarm_size swarm(i, 1, 1) = swarm(i, 1, 1) + swarm(i, 2, 1); %update x position swarm(i, 1, 2) = swarm(i, 1, 2) + swarm(i, 2, 2); %update y position % Update Position Bounds swarm(i, 1, 1) = max(swarm(i, 1, 1),LB(1)); swarm(i, 1, 1) = min(swarm(i, 1, 1),UB(1)); swarm(i, 1, 2) = max(swarm(i, 1, 2),LB(2)); swarm(i, 1, 2) = min(swarm(i, 1, 2),UB(2)); % The fitness function (Rastrigin) x = swarm(i, 1, 1); y = swarm(i, 1, 2); Fval(i)=20+x.^2-10.*cos(2.*3.14159.*x)+y.^2-10.*cos(2.*3.14159.*y); % evaluate the obejective function if Fval(i) < swarm(i, 4, 1) % if new position is better swarm(i, 3, 1) = swarm(i, 1, 1); % Update the position of the first dimension swarm(i, 3, 2) = swarm(i, 1, 2); % Update the position of the second dimension swarm(i, 4, 1) = Fval(i); % Update best value end end a=2-iter*((2)/Max_iterations); % a decreases linearly fron 2 to 0 % Search for the global best solution [temp, gbest] = min(swarm(:, 4, 1)); % global best position % Calculate the mean of the best three solutions in each dimension [aa, ind]=sort(Fval); for i=1:Dim M(i)=mean(swarm(ind(1:3), 1, i)); end r1=rand(); % r1 is a random number in [0,1] r2=rand(); % r2 is a random number in [0,1] A1=2*a*r1-a; C1=2*r2; % Updating velocity vectors for i = 1 : swarm_size if (rand<0.5) % Use Sine function to move for j=1:Dim swarm(i, 2, j) = (a)*sin(rand)*(swarm(i, 3, j) - swarm(i, 1, j))+(2-a)*sin(rand)*(M(j)- swarm(i, 1, j)); %x velocity component end else % Use Coine function to move for j=1:Dim swarm(i, 2, j) = (a)*cos(rand)*(swarm(i, 3, j) - swarm(i, 1, j))+(2-a)*cos(rand)*(M(j)- swarm(i, 1, j)); %x velocity component end end end % Store the best fitness valuye in the convergence curve ConvergenceCurve(iter,1)=swarm(gbest,4,1); disp(['Iterations No. ' int2str(iter) ' , the best fitness value is ' num2str(gbest)]); end HistoryConvCurve{r}=ConvergenceCurve; clear ConvergenceCurve end for i= 1: Runs plot(HistoryConvCurve{i}) ; hold on end title('Convergence Curve') xlabel('Iterations') ylabel('Fitness Value')