Skip to main content

Usage

Import​

from quickopt.pso import pso

pso​

Uses particle swarm optimization to maximize a function of double inputs.

Signature​

pso(funct, space_min, space_max, iterations=100, swarm_size=100, inertia=0.5, cognitive=1.0, social=2.0, clamp=0.1, verbose=1)

Parameters​

  • funct - function: (input: List[float]) -> float : The function to be maximized. It should take a list of doubles as input and return a double.

  • space_min - List[float] : Lower bound vector for the search space. Defines the minimum values for the parameters.

  • space_max - List[float] : Upper bound vector for the search space. Defines the maximum values for the parameters.

  • iterations - int, optional : The number of iterations to run the algorithm. Default is 100.

  • swarm_size - int, optional : The number of particles in the swarm. Default is 100.

  • inertia - double, optional : Inertia weight. Recommended to be in (0,1). Default is 0.5.

  • cognitive - double, optional : Cognitive weight. Recommended to be in [1,3]. Default is 1.0.

  • social - double, optional : Social weight. Recommended to be in [1,3]. Default is 2.0.

  • clamp - double, optional : Clamping limit on velocity. Default is 0.1.

  • verbose - int, optional : The verbosity level. 0 for final output, 1 for output at each iteration. Default is 1.

Output​

  • best_params - List[float]: The best set of parameters found by the particle swarm optimization process.

Notes​

  • Minimization can be achieved by returning the negative of objective function values.
  • The space_min and space_max vectors must be of the same size.
  • Ensure that the inertia is in (0,1), and the cognitive and social weights are in [1,3].
  • Adjust the verbosity level with the verbose parameter to control the amount of output during the iterations.