particle_swarm_optimization: Implementation of Particle Swarm Optimization (PSO)...

In niarules: Numerical Association Rule Mining using Population-Based Nature-Inspired Algorithms

Implementation of Particle Swarm Optimization (PSO) metaheuristic algorithm.

Description

This function uses PSO, a stochastic population-based optimization algorithm, to find the optimal numerical association rule.

Usage

particle_swarm_optimization(
  d = 10,
  np = 10,
  w = 0.7,
  c1 = 1.5,
  c2 = 1.5,
  nfes = 1000,
  features,
  data,
  is_time_series = FALSE
)

Arguments

d	Dimension of the problem (default: 10).
np	Population size (default: 10).
w	Inertia weight (default: 0.7).
c1	Cognitive coefficient (default: 1.5).
c2	Social coefficient (default: 1.5).
nfes	The maximum number of function evaluations (default: 1000).
features	A list containing information about features, including type and bounds.
data	A data frame representing instances in the dataset.
is_time_series	A boolean indicating whether the dataset is time series.

Value

A list containing the best solution, its fitness value, and the number of function evaluations and list of identified association rules.

References

Kennedy, J., & Eberhart, R. (1995). "Particle swarm optimization." Proceedings of ICNN'95 - International Conference on Neural Networks, 4, 1942–1948. IEEE. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/ICNN.1995.488968")}

niarules documentation built on Sept. 15, 2025, 5:08 p.m.