Opp repl parameter optimization

Parameter optimization

optimize_simulation_parameters() pairs a single simulation task with a scipy-based Nelder-Mead solver. At each iteration the optimizer picks new parameter values, runs the simulation, reads the target scalars, and minimizes the difference from the expected values. No gradients needed -- suitable for stochastic simulations.

Upstream reference: https://github.com/omnetpp/opp_repl/blob/main/doc/parameter_optimization.md

Requires the optimize extra (scipy, optimparallel):

pip install "opp_repl[optimize]"

Signature (keyword-only)

optimize_simulation_parameters(
    simulation_task,                   # result of get_simulation_task(...)
    expected_result_names=[...],       # scalar names to hit
    expected_result_values=[...],      # target values
    fixed_parameter_names=[...],       # optional -- held constant
    fixed_parameter_values=[...],
    fixed_parameter_assignments=[...], # INI-style path
    fixed_parameter_units=[...],
    parameter_names=[...],             # what to vary
    parameter_assignments=[...],       # INI-style path for each
    parameter_units=[...],             # format string(s); see below
    initial_values=[...],
    min_values=[...], max_values=[...],
)

Unit format strings

• Plain unit: "m", "Mbps" -- appended to the numeric value.
• Distribution wrapper: "exponential({0}s)" -- preserves an iaTime distribution while overriding its parameter.
• Any .format(value)-compatible string.

This matters because many OMNeT++ parameters are declared volatile and assigned distributions in the INI file. The unit wrapper keeps the distribution intact.

Example 1 — slotted ALOHA channel utilization

Maximize channel utilization in slotted ALOHA. Theoretical peak is 1/e ≈ 0.368 at iaTime ≈ 1.87 s. From overloaded start (0.5 s), converges in ~40 evaluations:

optimize_simulation_parameters(
    get_simulation_task(config_filter="SlottedAloha1",
                        sim_time_limit="10min"),
    expected_result_names=["channelUtilization:last"],
    expected_result_values=[0.368],
    fixed_parameter_names=[], fixed_parameter_values=[],
    fixed_parameter_assignments=[], fixed_parameter_units=[],
    parameter_names=["iaTime"],
    parameter_assignments=["Aloha.host[*].iaTime"],
    parameter_units=["exponential({0}s)"],
    initial_values=[0.5], min_values=[0.1], max_values=[20])

Output ends with:

Best: {'iaTime': 1.873} -> {'channelUtilization:last': 0.367}

Example 2 — INET WiFi range at 30 % PER

Find the distance where 54 Mbps WiFi reaches 30 % packet error. Converges to ~53.2 m in ~28 evaluations:

optimize_simulation_parameters(
    get_simulation_task(simulation_project=inet_project,
        working_directory_filter="showcases/wireless/errorrate",
        config_filter="General", run_number=0, sim_time_limit="1s"),
    expected_result_names=["packetErrorRate:vector"],
    expected_result_values=[0.3],
    fixed_parameter_names=["bitrate"], fixed_parameter_values=[54],
    fixed_parameter_assignments=["**.bitrate"],
    fixed_parameter_units=["Mbps"],
    parameter_names=["distance"],
    parameter_assignments=["*.destinationHost.mobility.initialX"],
    parameter_units=["m"],
    initial_values=[50], min_values=[20], max_values=[100])

Pitfalls

• Stochastic noise dominates near the optimum. Lengthen sim_time_limit or average several runs per evaluation (repeat count in the INI) when results get noisy.
• get_simulation_task() MUST match exactly one task. Ambiguous filters raise -- add run_number=0 or tighter filters.
• Bounds and initial values should straddle the expected optimum. Nelder-Mead can get stuck if the initial simplex is far from the feasible region.
• Each evaluation is a full simulation run; budget accordingly.
• Multi-dimensional optimization (several parameter_names) scales roughly O(n²) in simplex size; stay ≤ 4-5 parameters for sanity.

See also

• opp-repl-running-simulations — get_simulation_task() semantics.
• opp-repl-tasks-and-results — reading result scalars.
• opp-repl-ssh-cluster — distribute parallel evaluations.