pso

pso.js
======

Particle Swarm Optimisation library written in JS. Works with RequireJS, from a WebWorker, in node.js or in a plain browser environment.

Sample applications
-------------------

+ 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

Usage
-----

#### Basic usage case

``javascript
// 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:

`javascript
{
inertiaWeight: 0.8,
social: 0.4,
personal: 0.4,
pressure: 0.5
}
`

+ inertiaWeight is multiplied every frame with the previous velocity
+ social dictates how much a particle should be influenced by the best performing particle in the swarm
+ personal indicates how much a particle should be influenced by the best position it has been in
+ pressure` is the bias in selecting the best performing particle in the swarm

For more details consult the annotated source.