LONs-Numerical

Construction, visualisation and analysis of local optima networks (LONs) for numerical (continuous) optimisation.

This repository is associated to the following research article:

Contreras-Cruz, M.A., Ochoa, G., Ramirez-Paredes, J.P. (2020). Synthetic vs. Real-World Continuous Landscapes: A Local Optima Networks View. Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science, vol 12438. Springer, Cham.

https://doi.org/10.1007/978-3-030-63710-1_1

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Credits

• Concept: Gabriela Ochoa and Marcos Contreras-Cruz
• Implementation
  • Marcos Contreras-Cruz: Python code for LONs sampling using the Basin-hopping implementation provided in the SciPy library. Python code for selecting a suitable perturbation strength. C code for experimentation and integration.
  • Gabriela Ochoa: R code for generating and visualising the LON models as well as computing relevant metrics, using the igraph R library.
• Article write-up: Marcos Contreras-Cruz, Gabriela Ochoa and J.P. Ramirez-Paredes
• Repository code upload and readme/tutorial creation: Yuri Lavinas

Software requirements and libraries

• R: igraph, foreach, magick, plyr, rgl
• Python 3: numpy, scipy, pyDOE, deap (optional)

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Code Structure

The code is structured into 5 folders as follows:

1. BasinHopping

   • Python code for sampling local optima networks using the Basin-hopping algorithm.

2. Problems

   • Python file with optimisation problems/functions to analyse. You can add your own functions/optimisation problems here: problems.py file!
   • You need to change the functions:FunctionSelector and GetDomain. Add a function call to your problem.

3. stepSizeTuning

   • Python script to select a suitable step size (perturbation strength) for Basin Hopping algorithm following the idea of varying the step size until half of the steps attempted escape the starting basin of attraction. (page 6 of the paper).
   • For help call python3 stepSizeTuning.py --help

4. Experiments

   • C code files for running experiments and obtaining results.

   • Compile all the C code by typing make

     • Move to the Experiments/ folder, type make, wait. You should only need to do this once.

   • Create a configuration file inside the cfg/ folder.

     • There's a template file inside the cfg folder with relevant information.

   • Call the Convergence script

     • For help call ./Convergence

     • Example: to run the code for the SpreadSpectrumRadarPollyPhase problem, with 10 runs and 10000 evaluations (function calls), type:

       • ./Convergence cfg/SpreadSpectrumRadarPollyPhase.cfg 10 10000

   • Call the GenerateResults script.

     • For help call: ./GenerateResults

     • Example: to run the code for the SpreadSpectrumRadarPollyPhase problem, type:

       • ./GenerateResults cfg/ SpreadSpectrumRadarPollyPhase.cfg

5. Graph

   • R scripts for generating the LONs models and visualisation.

   • GraphMetrics.r is called from the C code on the Experiments folder automatically.

   • Data2GraphViz.r used to generate LONs and CMLONs visualisations

     • For help, call: Rscript Data2GraphViz.r

     • Use the data in the files at Result/something/data_filtered to generate the LONs and CMLONs.

     • Example:

       Rscript Data2GraphViz.r ../Result/SpreadSpectrumRadarPollyPhase/data_filtered/data_SpreadSpectrumRadarPollyPhasen13_p1.256600.txt ../Result/SpreadSpectrumRadarPollyPhase/LONs/