Certara.RDarwin (version 1.1.1)

pyDarwinOptionsPSO: Create options for the Particle Swarm Optimization (PSO) in pyDarwin.

Description

This function allows you to set various options specific to the Particle Swarm Optimization (PSO) in pyDarwin.

Usage

pyDarwinOptionsPSO(
  inertia = 0.4,
  cognitive = 0.5,
  social = 0.5,
  neighbor_num = 20,
  p_norm = 2,
  break_on_no_change = 5
)

Value

An object containing the specified options for the Particle Swarm Optimization (PSO) algorithm.

Arguments

inertia

A real value specifying the particle coordination movement as it relates to the previous velocity (commonly denoted as w). Default: 0.4

cognitive

A real value specifying the particle coordination movement as it relates to its own best known position (commonly denoted as c1). Default: 0.5

social

A real value specifying the particle coordination movement as it relates to the current best known position across all particles (commonly denoted as c2). Default: 0.5

neighbor_num

A positive integer specifying the number of neighbors that any particle interacts with to determine the social component of the velocity of the next step. A smaller number of neighbors results in a more thorough search (as the neighborhoods tend to move more independently, allowing the swarm to cover a larger section of the total search space) but will converge more slowly. Default: 20

p_norm

A positive integer specifying the Minkowski p-norm to use. A value of 1 is the sum-of-absolute values (or L1 distance) while 2 is the Euclidean (or L2) distance. Default: 2

break_on_no_change

A positive integer specifying the number of iterations used to determine whether the optimization has converged. Default: 5

Examples

# Create PSO options with default values
options <- pyDarwinOptionsPSO()

# Create PSO options with custom values
options <- pyDarwinOptionsPSO(inertia = 0.2,
                              cognitive = 0.8,
                              social = 0.7,
                              neighbor_num = 10)