Implementation and Results of a "Randomized Local Search for Two-Dimensional Bin Packing and a Negative Result for Frequency Fitness Assignment"

<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) applied to the two-dimensional bin packing problem with and without orientation. As benchmark dataset, we use the <code>beng</code>, <code>A</code>, and <code>class</code> instances from <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> as well as the four non-trivial Almost Squares in Almost Squares (<a href="https://math.vu.nl/~sbhulai/publications/data_analytics2016b.pdf"><code>Asqas</code></a>) instances</p> <p>These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Rui Zhao, Zhize Wu, Daan van den Berg, Matthias Th&uuml;rer, Tianyu Liang, Ming Tan, and Thomas Weise. Randomized Local Search for Two-Dimensional Bin Packing and a Negative Result for Frequency Fitness Assignment. In <em>16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24)</em>. November 20-24, 2024. Porto, Portugal. Set&uacute;bal, Portugal: SciTePress.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> and other benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Rui ZHAO (赵睿) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:zr1329142665@163.com">zr1329142665@163.com</a>.</p>