Particle Swarm Optimisation library written in JS. Works with RequireJS, from a WebWorker, in node.js or in a plain browser environment.
- simple A simple application that optimizes a one dimensional function
- simple-require The same as simple, except using RequireJS
- simple-node A simple node example
- automaton A more sophisticated application that adapts a mechanism for a specified output path. pso.js is launched in this case by web workers
- circles A simple application that optimizes a two dimensional function
- shape-fitting Optimizes the positioning of arbitrary shapes in a square
- pool Optimizes the breaking shot of a pool game
- async Example of an asynchronous objective function
- parameters Optimizer performance when varying its parameters
- meta-optimizer pso.js is used to optimize the parameters of another instance of pso which is optimizing the Rastrigin function
- walking-critter Optimizing a "walking" critter - another example of asynchronous objective functions
// create the optimizer
var optimizer = new pso.Optimizer();
// set the objective function
optimizer.setObjectiveFunction(function (x) { return -(x[0] * x[0] + x[1] * x[1]); });
// set an initial population of 20 particles spread across the search space *[-10, 10] x [-10, 10]*
optimizer.init(20, [{ start: -10, end: 10 }, { start: -10, end: 10 }]);
// run the optimizer 40 iterations
for (var i = 0; i < 40; i++) {
optimizer.step();
}
// print the best found fitness value and position in the search space
console.log(optimizer.getBestFitness(), optimizer.getBestPosition());####Optimizer parameters
Optimizer parameters can be set by calling the setOptions method before creating a population with the init method. Otherwise, the default parameters will be used.
The setOptions method takes a single map-like object - here are its default values:
{
inertiaWeight: 0.8,
social: 0.4,
personal: 0.4,
pressure: 0.5
}inertiaWeightis multiplied every frame with the previous velocitysocialdictates how much a particle should be influenced by the best performing particle in the swarmpersonalindicates how much a particle should be influenced by the best position it has been inpressureis the bias in selecting the best performing particle in the swarm
For more details consult the annotated source.