runACS: Wrapper function for the Ant Colony System.

In jakobbossek/acotsp: Ant Colony Optimization for the Travelling Salesperson Problem

Description

Offers a more R-like interface to the Ant Colony System (ACS) for the TSP.

Usage

runACS(x, n.ants = 10L, alpha = 1, beta = 2, rho = 0.1,
  att.factor = 1, local.pher.update.fun = NULL,
  monitor = makeNullMonitor(), ...)

Arguments

x	[Network] A network, i. e., a graph generated with the netgen package. See makeNetwork for details.
n.ants	[integer(1)] Number of ants. Positive integer.
alpha	[numeric(1)] This parameter decides how much influence the pheromones on an edge have on the selection of edges. Default is 1.
beta	[numeric(1)] This parameter decided how much influence the edge distances have on the selection of edges. Default is 2.
rho	[numeric(1)] “Pheromone evaporation coefficient” respectively “evaporation rate”. In each iteration the pheromones on edge (i,j) are first decreased by (1 - rho) before ants deposit their pheromones on it. Must be in (0, 1). Default is 0.1.
att.factor	[numeric(1)] This is the socalled “constant attractiveness factor”. Default is 1.
local.pher.update.fun	[function] Local (pheromone) update rule applied right after an ant crossed an edge. Default is NULL, which means no local update at all. This must be a function which expects a single parameter pher.
monitor	[AntsMonitor] Monitor object of type AntsMonitor. Determines how to visualize the optimization process. Default is not visualization. See makeConsoleMonitor for basic console output.
...	[any] Further parameters passed to control object, e.g., max.iter. See makeACOTSPControl.

Value

[AntsResult] S3 result object.

References

Dorigo, M. and Gambardella, L. M. (1997) Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. In IEEE Transactions on Evolutionary Computation, pp. 53-66.

See Also

Other ants_shortcuts: runAS; runMMAS

jakobbossek/acotsp documentation built on May 18, 2019, 9:08 a.m.