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Works with *RequireJS*, from a *WebWorker*, in *node.js* or in a plain browser environment.\n\nSample applications\n-------------------\n\n+ [simple](http://adrianton3.github.io/pso.js/examples/simple/simple.html) A simple application that optimizes a one dimensional function\n+ [simple-require](http://adrianton3.github.io/pso.js/examples/simple-require/simple-require.html) The same as *simple*, except using RequireJS\n+ [simple-node](http://adrianton3.github.io/pso.js/examples/node/node.js) A simple node example\n+ [automaton](http://adrianton3.github.io/pso.js/examples/automaton/automaton.html) A more sophisticated application that adapts a mechanism for a specified output path.\npso.js is launched in this case by web workers\n+ [circles](http://adrianton3.github.io/pso.js/examples/circles/circles.html) A simple application that optimizes a two dimensional function\n+ [shape-fitting](http://adrianton3.github.io/pso.js/examples/shape-fitting/shape-fitting.html) Optimizes the positioning of arbitrary shapes in a square\n+ [pool](http://adrianton3.github.io/pso.js/examples/pool/pool.html) Optimizes the breaking shot of a pool game\n+ [async](http://adrianton3.github.io/pso.js/examples/async/async.html) Example of an asynchronous objective function\n+ [parameters](http://adrianton3.github.io/pso.js/examples/parameters/parameters.html) Optimizer performance when varying its parameters\n+ [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\n+ [walking-critter](http://adrianton3.github.io/pso.js/examples/walker/walker.html) Optimizing a \"walking\" critter - another example of asynchronous objective functions\n\nUsage\n-----\n\n#### Basic usage case\n\n```javascript\n// create the optimizer\nvar optimizer = new pso.Optimizer();\n\n// set the objective function\noptimizer.setObjectiveFunction(function (x) { return -(x[0] * x[0] + x[1] * x[1]); });\n\n// set an initial population of 20 particles spread across the search space *[-10, 10] x [-10, 10]*\noptimizer.init(20, [{ start: -10, end: 10 }, { start: -10, end: 10 }]);\n\n// run the optimizer 40 iterations\nfor (var i = 0; i \u003c 40; i++) {\n    optimizer.step();\n}\n\n// print the best found fitness value and position in the search space\nconsole.log(optimizer.getBestFitness(), optimizer.getBestPosition());\n```\n\n####Optimizer parameters\n\nOptimizer parameters can be set by calling the `setOptions` method before creating a population with the `init` method. Otherwise, the default parameters will be used.\nThe `setOptions` method takes a single map-like object - here are its default values:\n\n```javascript\n\t{\n\t\tinertiaWeight: 0.8,\n\t\tsocial: 0.4,\n\t\tpersonal: 0.4,\n\t\tpressure: 0.5\n\t}\n```\n\n+ `inertiaWeight` is multiplied every frame with the previous velocity\n+ `social` dictates how much a particle should be influenced by the best performing particle in the swarm\n+ `personal` indicates how much a particle should be influenced by the best position it has been in\n+ `pressure` is the bias in selecting the best performing particle in the swarm\n\nFor more details consult the [annotated source](http://adrianton3.github.io/pso.js/docs/pso.html).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadrianton3%2Fpso.js","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadrianton3%2Fpso.js","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadrianton3%2Fpso.js/lists"}