Genetic Hybridization Placement

GeneticHybridizationPlacement is a Python-based ecosystem for the optimization of resource placement in fog computing environments using Genetic Algorithms (GAs). The framework supports both standalone (aka single-algorithm) and hybrid-algorithm evolutionary strategies, enabling comprehensive experimentation and benchmarking.

Project Overview

This project provides a flexible environment to:

• Run single GA algorithms (e.g., NSGA2, NSGA3, UNSGA3, SMSEMOA) independently on resource placement problems.
• Run hybrid GAs, where multiple algorithms cooperate or compete within the same evolutionary process, allowing for hybridization strategies and advanced metaheuristics.
• Analyze and compare the performance of single and hybrid approaches using a unified set of metrics and statistical tools.
• Automate experiments across different network topologies, objective combinations, and random seeds, with support for parallel execution and reproducibility.

Key Features

• Single Mode: Execute and analyze standard GAs individually for baseline performance.
• Hybrid Mode: Combine several GAs in a single run, leveraging their strengths for improved optimization.
• Experiment Automation: Shell scripts and Python tools for batch runs, normalization, and result aggregation.
• Statistical Analysis: Built-in scripts for statistical comparison, Pareto front computation, and visualization.

Getting Started

Clone the project

git clone ..

Dependencies

We recommend the use of uv python package manager. Install all required libraries with:

uv sync

Experiment running

The execution last long time the scripts and ppost-analysis is seperated in multiples files. It's in your on the orchestation and distribution in differents computational devices.

The parametrization realay in two files:

• mainly, in script_constants.sh
• some residual parameters in file: resopt/param/parameters.py

Modify these two bash scripts according with your experimentation:

• run standalone or single GA algorithms:

.\run_single.sh

• or run Hybrid configuration:

.\run_hybrids.sh

Once you have the results, you can compute the final reference points, which will be used to calculate performance metrics across different configurations and provide a baseline comparison between single and hybrid approaches. There are additional scripts available for generating various plots.

# Extract the reference points from:
uv run compute_unified_PF.py --control single 
uv run compute_unified_PF.py --control hybrids 
# Merge reference points from both execution, internally check if the previous files are computed
uv run compute_unified_PF.py --control merge 
# Merge hybridization population among scenarios
uv run merge_hybridization_cases.py 
# Compute the metrics in hybrid case and standalone scenario (aka standard term)
uv run compute_metrics_hybridization.py
uv run compute_metrics_standard.py 
# Plots
## a simple GD, IGD... convergence plots
uv run do_algorithms_plot_with_replicates.py
# Performance and plot the Wilcoxon test
uv run wilcoxon_statistical_analysis.py
# Plot the exchange of genetical material: stacked and line plot
uv run do_plot_genetical_crossing.py
uv run do_plot_genetical_crossing_line.py

Important:

• Follow the sequence, as it is necessary to normalize the results.
• You can also distribute the execution among different devices and only transfer the output data to maintain the performance of other scripts.
• These scripts use script_constants.sh to obtain reference variables (population size, number of generations, etc.), but you can include your own variables internally for testing.

Directory Structure

• resopt/ – Core optimization, problem definitions, and plotting modules.
• data/ – Output directories for experiment results.
• scripts/ – Shell scripts for running and managing experiments.
• test/ – Unit and integration tests.

Citation

If you use this ecosystem in your research, please cite the corresponding paper or contact the authors for more information.

pending

Project

HIDDEMS – PID2021-128071OB-I00 MICIU/AEI/10.13039/501100011033 and FEDER,UE (HIDDEMs)[https://hiddems.uib.cat]

Note

This project and repository are under continuous development.

Acknowledge

This project extends the original repo in terms of experimentation, analysis, and plotting.