Another Particle Swarm Toolbox: ... - File Exchange

Code covered by the BSD License

Highlights from
Another Particle Swarm Toolbox

...
... Find the minimum of a function using Particle Swarm Optimization.
... Particle swarm optimization for binary genomes.
...
... This is like "soft" boundaries, except that some kind of penalty value
ackleysfcn(x) Ackley's Function.
binarytestfcn(x)
dejongsfcn(x)
dropwavefcn(x)
evolutioncomplete(options,sta... Plays a notification when genetic algorithm finishes. Requires a
griewangksfcn(x) Template for writing custom test/demonstration functions for psodemo.
langermannsfcn(x)
nonlinearconstrdemo(x) Nonlinear constraints demo with Rosenbrock's function. Select this
psoboundsabsorb(state,Aineq,b...
psocalculatepenalties(state) Calculates the penalty to apply for out-of-bounds particles when using
psodemo(DemoMode) Runs the PSO on a few demonstration functions, which should be located
psoiterate(options,state,flag... Updates swarm positions and velocities. Called to iterate the swarm from
psooptimset(varargin) Creates an options structure for pso.
psoplotbestf(options,state,fl... Plots the best, mean, and worst scores of particle swarm.
psoplotscorediversity(options... Plots a histogram containing the best and mean scores of particle swarm.
psoplotswarm(options,state,fl... Plots the positions of particle swarm.
psoplotswarmsurf(options,stat... Shows the evolution of a 2-variable population over a known surface. This
rastriginsfcn(x) Takes row inputs. If not row, will attempt to correct it.
rosenbrocksfcn(x) Takes row inputs. If input is not row, will attempt to correct it.
schwefelsfcn(x) Schwefel's function.
templatefcn(x) Template for writing custom test/demonstration functions for psodemo.
testfcn1(x)
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from Another Particle Swarm Toolbox by Sam
Implementation of a PSO algorithm with the same syntax as the Genetic Algorithm Toolbox.

...

function state = ...
    psoboundsreflect(state,Aineq,bineq,Aeq,beq,LB,UB,nonlcon,options)

x = state.Population ;
v = state.Velocities ;

for i = 1:size(state.Population,1)
    lowindex = [] ; highindex = [] ;
    if ~isempty(LB), lowindex = x(i,:) < LB ; end
    if ~isempty(UB), highindex = x(i,:) > UB ; end
    
    x(i,lowindex) = LB(lowindex) ;
    x(i,highindex) = UB(highindex) ;
    v(i,lowindex) = -v(i,lowindex) ;
    v(i,highindex) = -v(i,highindex);
end % for i

state.Population = x ;
state.Velocities = v ;

Highlights from Another Particle Swarm Toolbox

Highlights from
Another Particle Swarm Toolbox