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opp-repl-parameter-optimization

Optimize OMNeT++ simulation parameters with SciPy Nelder-Mead to meet target metrics.

Updated Apr 23, 2026
Search Tags:#optimization#simulation#scipy#parameter-tuning#opp_repl#nelder-mead
One-click install
npx skills add https://github.com/tabgab/opp_repl-skill --skill opp-repl-parameter-optimization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: opp-repl-parameter-optimization
Source: https://github.com/tabgab/opp_repl-skill/tree/main/opp-repl-parameter-optimization
Command: npx skills add https://github.com/tabgab/opp_repl-skill --skill opp-repl-parameter-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables automated tuning of simulation parameters to achieve targeted outcomes using a derivative-free optimization approach (SciPy Nelder-Mead) within opp_repl-managed OMNeT++ simulations. It iterates over a single SimulationTask, adjusting parameter values until the expected result vectors are met, accommodating stochastic variability and non-differentiable models.

Core Features & Use Cases

  • Automated parameter search: jointly tunes multiple parameters to meet specified scalars such as throughput, error rate, or utilization.
  • Flexible task definition: works with get_simulation_task(...) and supports fixed_parameter_names/values, parameter_names/assignments, initial_values, min_values, max_values, and units to preserve semantics.
  • Stochastic-aware optimization: designed for noisy simulations; guidance includes longer runs or repeated evaluations to stabilize results.
  • Use Case: tune an OMNeT++ configuration until the observed metrics align with target values within an acceptable tolerance.

Quick Start

Configure a SimulationTask with your targets and call optimize_simulation_parameters to start tuning.

Frequently Asked Questions about opp-repl-parameter-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I tune OMNeT++ simulation parameters to hit target throughput or error rate?▼

To automate parameter tuning for stochastic OMNeT++ simulations, configure a SimulationTask with your targets, specify initial values and bounds, then call the optimization function to start adjusting parameters using the SciPy Nelder-Mead algorithm.

What is derivative-free parameter tuning for stochastic simulations?▼

Derivative-free parameter tuning for stochastic simulations is an optimization method that adjusts parameters without requiring model gradients, making it suitable for non-differentiable and noisy OMNeT++ simulation models.

Does this parameter optimization approach work with noisy simulation results?▼

Yes, this parameter optimization approach works with noisy simulation results by accommodating stochastic variability; guidance includes using longer simulation runs or repeated evaluations to stabilize results and achieve reproducible optimization.

Do I need SciPy to run Nelder-Mead optimization on OMNeT++ simulations?▼

Yes, you need SciPy to run Nelder-Mead optimization on OMNeT++ simulations, as the skill requires the optimize extra (SciPy Nelder-Mead) to perform derivative-free parameter search across stochastic runs.

Can I fix certain parameters while tuning others in a SimulationTask?▼

Yes, you can fix certain parameters while tuning others in a SimulationTask by specifying fixed_parameter_names and fixed_parameter_values, while defining adjustable parameters through parameter_names, initial_values, and min/max bounds.

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