JS粒子群优化器_JavaScript_代码_下载

pso.js ===== [](https://gitter.im/kotsoft/psojs?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) **pso.js** aims to be a clean and tiny implementation of [Particle Swarm Optimization](http://en.wikipedia.org/wiki/Particle_swarm_optimization). I am currently using it in my neural network library to optimize weights. However, it's flexible enough to be used in many different kinds of optimization problems. Check out [my blog post on CodePen](http://codepen.io/kotsoft/blog/psodemos) to see two examples on how this library can be used. Usage ----- Creating a swarm is simple. All you need to do is specify the number of particles and the number of parameters you want to optimize. The last argument is the options hash. ```javascript var numParticles = 30; var numParams = 20; var swarm = new PSO.swarm(numParticles, numParams, { min: -1, max: 1, velocityMultiplier: .1, springCoefficient: .25, bounceCoefficient: .25, enableJitter: false, jitterRatio: .01, fitnessCompare: function(a, b) { return a < b; } }); ``` The final step is to create an iteration loop. ```javascript while (true) { for (var i in swarm.particles) { var fitness = yourMethodOfDeterminingFitness(); swarm.particles[i].setFitness(fitness); } swarm.update(); } ``` Parameters and Options ----- `numParticles` Usually the recommended number is 30. I've gotten away with using fewer, so definitely play around with it to see what works for your particular problem. `numParams` The number of parameters you want to optimize. For a neural network, it would be the total number of weights and biases. `min` and `max` Use these to define the range you want to search over. **Warning:** I am using a soft bounce at the edges so values may go over slightly. `velocityMultiplier` How fast you want the particles to go in proportion to the search range. `springCoefficient` Unless you want to slow things down so you can see the behavior more clearly, I would recommend keeping it as is. Going over will keep it from converging and going lower doesn't improve the search much. `bounceCoefficient` Increase to 1 for a harder boundary. Set it to 0 if you want no boundary at all. `enableJitter` and `jitterRatio` For more difficult problems where you find yourself getting stuck at local optima, jittering really helps. When I was using PSO for neural network regression, jittering often helped me converge with errors an order of magnitude lower than with it turned off. `fitnessCompare` Just define a function that returns true when `a` is considered more fit than `b`. The default returns `true` when `a < b`, which is useful for error minimization problems.