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https://github.com/adrianton3/pso.js

Particle swarm optimization library
https://github.com/adrianton3/pso.js

global-optimization particle-swarm-optimization pso

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Particle swarm optimization library

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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](http://adrianton3.github.io/pso.js/examples/simple/simple.html) A simple application that optimizes a one dimensional function
+ [simple-require](http://adrianton3.github.io/pso.js/examples/simple-require/simple-require.html) The same as *simple*, except using RequireJS
+ [simple-node](http://adrianton3.github.io/pso.js/examples/node/node.js) A simple node example
+ [automaton](http://adrianton3.github.io/pso.js/examples/automaton/automaton.html) A more sophisticated application that adapts a mechanism for a specified output path.
pso.js is launched in this case by web workers
+ [circles](http://adrianton3.github.io/pso.js/examples/circles/circles.html) A simple application that optimizes a two dimensional function
+ [shape-fitting](http://adrianton3.github.io/pso.js/examples/shape-fitting/shape-fitting.html) Optimizes the positioning of arbitrary shapes in a square
+ [pool](http://adrianton3.github.io/pso.js/examples/pool/pool.html) Optimizes the breaking shot of a pool game
+ [async](http://adrianton3.github.io/pso.js/examples/async/async.html) Example of an asynchronous objective function
+ [parameters](http://adrianton3.github.io/pso.js/examples/parameters/parameters.html) Optimizer performance when varying its parameters
+ [meta-optimizer](http://adrianton3.github.io/pso.js/examples/meta/meta.html) pso.js is used to optimize the parameters of another instance of pso which is optimizing the Rastrigin function
+ [walking-critter](http://adrianton3.github.io/pso.js/examples/walker/walker.html) 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](http://adrianton3.github.io/pso.js/docs/pso.html).