POS_PSO

Highly Configurable Particle Swarm Optimizer implemented in pure Rust

Easily mimimize a cost function across multiple threads

• Multiple optimizations can be run in parallel with Independant Swarms
• or Swarms can periodically share best-known locations in the search space with Collaborative Swarms

Motion dynamics are loosly based on Matlab's Particle Swarm Algorithm

Optimizer takes a closure with the following header as the cost function:

move |x: &[f64]| -> f64 {}

• The input slice-array represents a point in the N dimensional optimization space
• The returned cost is used to navigate the search space to locate a minimum
• currently only supports f64, but future updates may allow more generic cost-functions

Some Examples to get started:

Single-Threaded Example (Single Swarm)

• Use the default independant swarm configuration (good place to start, but you may want to define your own based on your use case)
• Search Space Bounds and Max Velocities still must be defined in a JobConfig object
• Single-Swarm may be the fastest performing option for high-end intel processors

extern crate pos_pso;
use pos_pso::{PSO, PSOConfig, JobConfig};

fn main() {
    let num_variables = 5;

    // Define a cost function to minimize:

    //data captured by closure
    let mins = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let coeffs = vec![0.25; num_variables];

    //cost function closure must take a &[f64] and return f64
    let cost_function = move |point: &[f64]| -> f64 {
        let mut sum = 0.0;
        for i in 0..num_variables {
            sum += (point[i] - mins[i]).powi(2) * coeffs[i];
        }
        sum
    };

    // Create a PSO Configuration:
    let pso_config = PSOConfig::new(
        1,          // 1 swarm used in optimization
        256,        // 256 particles are spawned
        10,         // console is updated every 10 itterations
        true        // optimizer is verbose (will provide more detailed information to console)
    );


    // Create a PSO:
    let pso = PSO::new(pso_config);


    // Create a Job Configuration:
    let job_config = JobConfig::new(
        num_variables, 
        vec![[-15.0, 15.0]; num_variables],  // [upper, lower] bounds for each variable
        vec![1.125; num_variables],          // max velocity for each variable
        100,                                 // run for 100 itterations
        0.0000001,                           // exit cost (optimization will stop when a cost of 0.0000001 is reached)
    );


    // Minimize cost function:

    //use minimize_independant to optimize with the default independant-swarm configuration
    //the next example will show how to use collaborative-swarms
    let min = pso.minimize_independant(job_config, cost_function);

    println!("Minimum of: {}, With value: {:?}", min.0, min.1);
}

Multi-Threaded Example* Use 4 collaborative swarms with fewer particles in each)

• A custom swarm configuration is defined with motion coefficients and a record-sharing period
• This may be the fastest performing option on high-end AMD processors

let num_variables = 5;

    // Define a cost function to minimize:

    //data captured by closure
    let mins = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let coeffs = vec![0.25; num_variables];

    //cost function closure must take a &[f64] and return f64
    let cost_function = move |point: &[f64]| -> f64 {
        let mut sum = 0.0;
        for i in 0..num_variables {
            sum += (point[i] - mins[i]).powi(2) * coeffs[i];
        }
        sum
    };

    // Create a PSO Configuration:
    let pso_config = PSOConfig::new(
        4,          // 4 swarms (each spawned on their own thread)
        64,         // 64 particles per swarm
        10,         // console is updated every 10 itterations
        true        // optimizer is verbose (will provide more detailed information to console)
    );


    // Create a PSO:
    let pso = PSO::new(pso_config);

    
    // Create a Job Configuration:
    let job_config = JobConfig::new(
        num_variables, 
        vec![[-15.0, 15.0]; num_variables],  // [upper, lower] bounds for each variable
        vec![1.125; num_variables],          // max velocity for each variable
        100,                                 // run for 100 itterations
        0.0000001,                           // exit cost (swarms will stop when a cost of 0.0000001 is reached)
    );


    // Create a custom Swarm Configuration:

    //collaborative swarms will share information with eachother about best known locations in the search space
    let swarm_config = SwarmConfig::new_collaborative(
        1.45,       // local weigth:    how much particles care about their best known location
        1.6,        // tribal weight:   how much particles care about their swarms best known location
        1.25,       // global weight:   how much particles care about the overall best known location
        0.4,        // inertial coefficient:    component of a particles velocity that contributes to its next velocity
        1.25,       // inertial growth factor:  how much inertia grows and shrinks throughout optimization
        0.125,      // wall bounce factor:      component of velocity that is saved when particle goes out of bounds
        10,         // tribal-global collab period:   swarms share best known location every 10 itterations
    );


    // Minimize cost function:

    //use minimize to optimize with a custom SwarmConfig
    let min = pso.minimize(job_config, swarm_config, cost_function);

    println!("Minimum of: {}, With value: {:?}", min.0, min.1);

Advanced Example

• Use a group of 8 independant swarms to minimize the same cost function with different swarm parameters
• This example shows how to use a SwarmConfigDistribution where swarm-behavior coefficients are sampled from a user defined distribution
• All 8 swarms run in parallel on seperate threads (the lowest cost and best location are returned after all threads have finished)

extern crate pos_pso;
use pos_pso::{PSO, PSOConfig, JobConfig, SwarmConfigDistribution, ParamDist};

fn main() {
    let num_variables = 5;

    // Define a cost function to minimize:

    //data captured by closure
    let mins = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let coeffs = vec![0.25; num_variables];

    //cost function closure must take a &[f64] and return f64
    let cost_function = move |point: &[f64]| -> f64 {
        let mut sum = 0.0;
        for i in 0..num_variables {
            sum += (point[i] - mins[i]).powi(2) * coeffs[i];
        }
        sum
    };


    // Create a PSO Configuration:
    let pso_config = PSOConfig::new(
        8,          // 8 swarms (each spawned on their own thread)
        128,        // 128 particles per swarm
        10,         // console is updated by each thread every 10 itterations
        true        // optimizer is verbose (will provide more detailed information to console)
    );


    // Create a PSO:
    let pso = PSO::new(pso_config);


    // Create a Job Configuration:
    let job_config = JobConfig::new(
        num_variables, 
        vec![[-15.0, 15.0]; num_variables],  // [upper, lower] bounds for each variable
        vec![1.125; num_variables],          // max velocity for each variable
        100,                                 // run for 100 itterations
        0.0000001,                           // exit cost (swarms will stop when a cost of 0.0000001 is reached)
    );


    // Create a Swarm Configuration Distribution:

    //the optimizer will sample a new swarm configuration for each swarm
    //this is usefull for automatically creating a range of swarm behaviors
    //in this case we are using new_independant, so all 8 optimizations will run seperately in parallel
    let swarm_config = SwarmConfigDistribution::new_independant(
        ParamDist::Fixed(1.45),                 // local: fixed value of 1.45
        ParamDist::Range([1.65, 0.25]),         // tribal: random value: 1.65 +/- 25% 
        ParamDist::Fixed(0.4),                  // momentum: fixed value of 0.4
        ParamDist::Range([1.25, 0.05]),         // momentum growth factor: random value: 1.25 +/- 5%
        ParamDist::Fixed(0.0125),               // wall bounce factor: fixed value of 0.0125
    );


    // Minimize cost function:

    //use minimize_distributed to accept SwarmConfigDistribution
    let min = pso.minimize_distributed(job_config, swarm_config, cost_function);

    println!("Minimum of: {}, With value: {:?}", min.0, min.1);

}