particle-swarm

You optimize continuous, high-dimensional objective functions by simulating a swarm of candidate solutions that collaboratively searches for optima. This particle swarm intelligence approach is applicable to parameter tuning, function optimization, and surrogate-model fitting.

Particle swarm optimization (PSO) solves continuous optimization problems by having a swarm of candidate solutions collaboratively search for optima. It adjusts particle velocities based on cognitive and social coefficients, guiding the swarm toward the best-known positions.

Yes, the particle swarm optimization engine supports configurable inertia weight, cognitive and social coefficients, boundary handling, and velocity clamping. It also includes an optional adaptive inertia feature with convergence tracking.

Yes, particle swarm optimization is well-suited for calibrating model parameters within specified bounds. You define the objective function and bounds for each decision variable, then the swarm searches the constrained space to find the optimal parameters.

Particle swarm optimization achieves fast convergence on smooth objectives with a small set of tunable parameters, but may struggle with highly irregular search spaces. Velocity clamping and adaptive inertia help manage convergence, but complex multi-modal functions can still pose challenges.

To start using a PSO engine, define your objective function and bounds for each decision variable. Instantiate the optimizer with your desired number of particles, then call the optimize function to obtain the best solution and convergence history.