diff --git a/R/OptimizerBatchChain.R b/R/OptimizerBatchChain.R
index d45b10126..2b288a0a1 100644
--- a/R/OptimizerBatchChain.R
+++ b/R/OptimizerBatchChain.R
@@ -142,8 +142,8 @@ OptimizerBatchChain = R6Class("OptimizerBatchChain", inherit = OptimizerBatch,
         optimizer = private$.optimizers[[i]]
         optimizer$param_set$values = self$param_set$.__enclos_env__$private$.sets[[i]]$values
         optimizer$optimize(inner_inst)
-        inner_inst$archive$data$batch_nr = max(inst$archive$data$batch_nr, 0L) + inner_inst$archive$data$batch_nr
-        inner_inst$archive$data$optimizer = private$.ids[i]
+        set(inner_inst$archive$data, j = "batch_nr", value = max(inst$archive$data$batch_nr, 0L) + inner_inst$archive$data$batch_nr)
+        set(inner_inst$archive$data, j = ".optimizer_id", value = private$.ids[i])
         inst$archive$data = rbind(inst$archive$data, inner_inst$archive$data, fill = TRUE)
         inner_inst$archive$data = data.table()
         if (terminator$is_terminated(inst$archive)) {
diff --git a/man/Objective.Rd b/man/Objective.Rd
index 0902467fd..c00d62437 100644
--- a/man/Objective.Rd
+++ b/man/Objective.Rd
@@ -35,9 +35,6 @@ stored and accessed here. Set constant values are passed to \verb{$.eval()}
 and \verb{$.eval_many()} as named arguments.}
 
 \item{\code{check_values}}{(\code{logical(1)})\cr}
-
-\item{\code{packages}}{(\code{character()})\cr
-Additional packages that are required to evaluate the objective function.}
 }
 \if{html}{\out{</div>}}
 }
@@ -57,6 +54,9 @@ Dimension of domain.}
 
 \item{\code{ydim}}{(\code{integer(1)})\cr
 Dimension of codomain.}
+
+\item{\code{packages}}{(\code{character()})\cr
+Set of required packages.}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/tests/testthat/test_OptimizerChain.R b/tests/testthat/test_OptimizerChain.R
index 3d51163f2..1664f2ce8 100644
--- a/tests/testthat/test_OptimizerChain.R
+++ b/tests/testthat/test_OptimizerChain.R
@@ -4,32 +4,32 @@ test_that("OptimizerBatchChain", {
     terminators = list(trm("evals", n_evals = 10L), trm("evals", n_evals = 10L)))
   expect_class(z$optimizer, "OptimizerBatchChain")
   expect_output(print(z$optimizer), "OptimizerBatchChain")
-  expect_identical(z$instance$archive$data[optimizer == "OptimizerBatchRandomSearch"]$batch_nr, 1:10)
-  expect_identical(z$instance$archive$data[optimizer == "OptimizerBatchGridSearch"]$batch_nr, 11:20)
+  expect_identical(z$instance$archive$data[.optimizer_id == "OptimizerBatchRandomSearch"]$batch_nr, 1:10)
+  expect_identical(z$instance$archive$data[.optimizer_id == "OptimizerBatchGridSearch"]$batch_nr, 11:20)
 
   z = test_optimizer_2d("chain", term_evals = 20L,
     optimizers = list(opt("random_search"), opt("grid_search")),
     terminators = list(trm("evals", n_evals = 10L), trm("evals", n_evals = 10L)))
   expect_class(z$optimizer, "OptimizerBatchChain")
   expect_output(print(z$optimizer), "OptimizerBatchChain")
-  expect_identical(z$instance$archive$data[optimizer == "OptimizerBatchRandomSearch"]$batch_nr, 1:10)
-  expect_identical(z$instance$archive$data[optimizer == "OptimizerBatchGridSearch"]$batch_nr, 11:20)
+  expect_identical(z$instance$archive$data[.optimizer_id == "OptimizerBatchRandomSearch"]$batch_nr, 1:10)
+  expect_identical(z$instance$archive$data[.optimizer_id == "OptimizerBatchGridSearch"]$batch_nr, 11:20)
 
   z = test_optimizer_2d("chain", term_evals = 20L,
     optimizers = list(opt("random_search", batch_size = 10L), opt("grid_search", batch_size = 10L)),
     terminators = list(trm("evals", n_evals = 10L), trm("evals", n_evals = 10L)))
   expect_class(z$optimizer, "OptimizerBatchChain")
   expect_output(print(z$optimizer), "OptimizerBatchChain")
-  expect_identical(unique(z$instance$archive$data[optimizer == "OptimizerBatchRandomSearch"]$batch_nr), 1L)
-  expect_identical(unique(z$instance$archive$data[optimizer == "OptimizerBatchGridSearch"]$batch_nr), 2L)
+  expect_identical(unique(z$instance$archive$data[.optimizer_id == "OptimizerBatchRandomSearch"]$batch_nr), 1L)
+  expect_identical(unique(z$instance$archive$data[.optimizer_id == "OptimizerBatchGridSearch"]$batch_nr), 2L)
 
   z = test_optimizer_dependencies("chain", term_evals = 20L,
     optimizers = list(opt("random_search"), opt("grid_search")),
     terminators = list(trm("evals", n_evals = 10L), trm("evals", n_evals = 10L)))
   expect_class(z$optimizer, "OptimizerBatchChain")
   expect_output(print(z$optimizer), "OptimizerBatchChain")
-  expect_identical(z$instance$archive$data[optimizer == "OptimizerBatchRandomSearch"]$batch_nr, 1:10)
-  expect_identical(z$instance$archive$data[optimizer == "OptimizerBatchGridSearch"]$batch_nr, 11:20)
+  expect_identical(z$instance$archive$data[.optimizer_id == "OptimizerBatchRandomSearch"]$batch_nr, 1:10)
+  expect_identical(z$instance$archive$data[.optimizer_id == "OptimizerBatchGridSearch"]$batch_nr, 11:20)
 
   # random restarts
   terminator = trm("none")
@@ -45,7 +45,7 @@ test_that("OptimizerBatchChain", {
     terminators = list(trm("evals", n_evals = 10L), trm("evals", n_evals = 10L)),
     real_evals = 20L
   )
-  expect_identical(unique(z$instance$archive$data$optimizer), c("OptimizerBatchGenSA", "OptimizerBatchGenSA_1"))
+  expect_identical(unique(z$instance$archive$data$.optimizer_id), c("OptimizerBatchGenSA", "OptimizerBatchGenSA_1"))
 
   # packages, properties, param_set, etc.
   optimizer = OptimizerBatchChain$new(optimizers = list(opt("random_search"), opt("gensa")))