Adding logical constraints to a function

In this example, I am trying to optimize the custom function "my_subset_mean" by using the "nb" (custom) function and the "TAopt" algorithm:

library(dplyr)
#create data
df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),
c = rnorm(100,10,10))

a <- c("a", "b", "c", "d", "e")
a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))
df$a <- a

e <- c("a", "b", "c", "d", "e")
e <- sample(e, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))
df$a <- e


#create function to be optimized
my_subset_mean <- function(x){  
    subset <- df %>% filter(a %in% names(x$r1)[x$r1], e %in% names(x$r4)[x$r4],
                            b > x$r2,
                            d < x$r3)
    ans <- -mean(subset$c)
    if (!is.finite(ans))
        ans <- 100
    ans
}


#store values of categorical variables into temporary objects

tmp <- !logical(length(sort(unique(a))))
names(tmp) <- sort(unique(a))

tmp1 <- !logical(length(sort(unique(e))))
names(tmp1) <- sort(unique(e))

x <- list(r1 = tmp, r4 = tmp1,
          r2 = 0.5,
          r3 = 0.5)




### optimization
nb <- function(x) {
    i <- sample(c("r1", "r2", "r3", "r4"), 1)
    if (i == "r1" & i == "r4") {
        j <- sample(length(x[[i]]), 1)
        x[[i]][j] <- !x[[i]][j]        
    } else {
        x[[i]] <- x[[i]] + runif(1, min = -0.1, max = 0.1)
        x[[i]] <- max(min(1, x[[i]]), 0)        
    }
    x
}


library("NMOF")
ans <- TAopt(my_subset_mean, list(x0 = x, neighbour = nb, nI = 1000))

-my_subset_mean(ans$xbest)

Can someone please tell me - in this process, where would you do specify the upper and lower bounds for r2 and r3? For example, if I want to specify that r2 between (0,2) and r3 between (0,1.5) - where exactly can I specify this?

Thanks!