Classical minimization methods, like gradient descent or quasi-Newton techniques,have been proved to struggle in dealing with optimization problems with a high-dimensional search space or subject to complex nonlinear constraints. In last decade, the interest on metaheuristic nature inspired algorithms has been growing steadily, due to their flexibility and effectiveness. EmiR is a package for R which implements several methauristic algorithms for optimization problems:
Unlike other available tools, EmiR can be used not only for unconstrained problems, but also for problems subjected to inequality constraints and for integer or mixed-integer problems.
How to citeIf you use EmiR, please cite the following work:
Example of usagePagano, D. and Sostero, L., «EmiR: Evolutionary Minimization for R», SoftwareX 18, 101083 (2022), doi: 10.1016/j.softx.2022.101083.
miele_cantrell <- function(x) {
x1 <- x[1]
x2 <- x[2]
x3 <- x[3]
x4 <- x[4]
value <- (exp(-x1) - x2)^4 + 100*(x2 - x3)^6 + (tan(x3 - x4))^4 + x1^8
return(value)
}
p1 <- parameter("x1", -2, 2, FALSE)
p2 <- parameter("x2", -2, 2, FALSE)
p3 <- parameter("x3", -2, 2, FALSE)
p4 <- parameter("x4", -2, 2, FALSE)
conf_algo <- config_bat(iterations = 200, population_size = 100)
results <- minimize(algorithm_id = "BAT",
obj_func = miele_cantrell,
parameters = list(p1, p2, p3, p4),
config = conf_algo)
print(results)RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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