Particle Swarm Optimization

Function for searching for minimizer of a function

% This minimizes the function fun, subject to the inequality constraints

% funCInEq and Equality constrants funCEq within the hyperbox between lb

% and ub

% fun = function handle for the function to be minimized

% funCInEq = function handle for the inequality condition (set to

% empty matrix when there is no inequality condition)

% funCEq = function handle for the equality condition (set to

% empty matrix when there is no equality condition)

% lb = vector of lower bounds

% ub = vector of upper bounds

% penalty = scale violation of constrain conditions (usually between 10

% and 100)

% popsize = population size (usually 30 to 50)

% maxiter = maximum iteration(less than 500)

% maxrun = maximum number of runs( 10 or less)

%% %%%%%%%%%%%%%%%% Examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% 1.

% Roosenbrook = @(x) (1 - x(1))^2 + 100*(x(2) - x(1)^2)^2;

% PartSwamOpt(Roosenbrook, [], [], [-2;-1], [2;3], 0, 30, 500, 10);

% 2.

% Adjiman = @(x) cos(x(1))*sin(x(2)) – x(1)/(x(2)^2 + 1);

% PartSwamOpt(Adjiman, [], [], [-1;-1], [2;1], 0, 30, 500, 10);

% 3.

% Beale = @(x) (1.5-x(1) + x(1)*x(2))^2 + (2.25-x(1) + x(1)*x(2)^2)^2 + (2.625-x(1) + x(1)*x(2)^3)^2;

% PartSwamOpt(Beale, [], [], [-1;-1], [2;1], 0, 30, 500, 10);