1 | initial version |
Rmoo is a package in R that provides a framework for multiobjective optimization. It allows users to optimize multiple objectives simultaneously. Here is an example of how you can use rmoo for multiobjective optimization in R:
install.packages("rmoo")
library(rmoo)
fun1 <- function(x) { x[1]^2 + x[2]^2 }
fun2 <- function(x) { (x[1] - 1)^2 + x[2]^2 }
In this example, there are two objectives, fun1
and fun2
, which take in a two-dimensional vector x
and return a scalar value.
problem <- makeMultiObjectiveProblem(
objectives = list(fun1, fun2),
n.variables = 2,
bounds = list(c(-5, 5), c(-5, 5)),
names = c("x1", "x2")
)
makeMultiObjectiveProblem
creates a problem instance with the specified objectives, number of variables, bounds, and variable names. In this example, the problem has two variables, x1
and x2
, each with a range of -5 to 5.
result <- mco(problem, algorithm = "nsga2")
In this example, mco
is the function that performs the optimization, and nsga2
is the algorithm used for the optimization. The result is a list of solutions, where each solution is a vector of the values of x1
and x2
that optimize the objectives.
plot(result, labels = TRUE)
This will show a scatter plot of the solutions, where each point represents a solution that optimizes the objectives.
Overall, rmoo can be used to optimize multiple objectives simultaneously in R, allowing users to find the trade-offs between objectives and choose the most appropriate solution.