{"id":16651091,"url":"https://github.com/eric-bradford/ts-emo","last_synced_at":"2025-09-03T15:31:01.777Z","repository":{"id":201737193,"uuid":"112434809","full_name":"Eric-Bradford/TS-EMO","owner":"Eric-Bradford","description":"This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).","archived":false,"fork":false,"pushed_at":"2020-06-19T15:03:41.000Z","size":1918,"stargazers_count":94,"open_issues_count":1,"forks_count":16,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-12-30T01:41:58.672Z","etag":null,"topics":["bayesian-optimization","black-box-optimization","expensive-to-evaluate-functions","gaussian-processes","genetic-algorithms","kriging","machine-learning","matlab","multi-objective-optimization","spectral-sampling","surrogate-based-optimization","thompson-sampling"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Eric-Bradford.png","metadata":{"files":{"readme":"README.md","changelog":"ChangeLog.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-11-29T06:13:48.000Z","updated_at":"2024-12-19T01:58:14.000Z","dependencies_parsed_at":null,"dependency_job_id":"3f4abfd6-d72d-4a1d-b7e1-8ae547174ac5","html_url":"https://github.com/Eric-Bradford/TS-EMO","commit_stats":null,"previous_names":["eric-bradford/ts-emo"],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FTS-EMO","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FTS-EMO/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FTS-EMO/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FTS-EMO/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Eric-Bradford","download_url":"https://codeload.github.com/Eric-Bradford/TS-EMO/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231895946,"owners_count":18442389,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-optimization","black-box-optimization","expensive-to-evaluate-functions","gaussian-processes","genetic-algorithms","kriging","machine-learning","matlab","multi-objective-optimization","spectral-sampling","surrogate-based-optimization","thompson-sampling"],"created_at":"2024-10-12T09:23:41.300Z","updated_at":"2024-12-30T18:18:14.903Z","avatar_url":"https://github.com/Eric-Bradford.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Thompson sampling efficient multiobjective optimization\nThis repository contains the source code for the “Thompson sampling efficient multiobjective optimization” (TSEMO) algorithm outlined in [(Bradford et al., 2018)](#Bradford2018). The algorithm is written to optimize expensive, black-box functions involving multiple conflicting criteria by employing Gaussian process surrogates. It is often able to determine a good approximation of the true Pareto front in signficantly less iterations than genetic algorithms. To cite TSEMO use [(Bradford et al., 2018)](#Bradford2018).\n\n\u003cimg src=\"/Old_versions/Images/GP_sample_graphs.jpg\" width=\"400\"\u003e\n\n## Getting started\nTo use TSEMO download all files contained in the repository and run the algorithm on the required test-function as shown in the example matlab file [TSEMO_Example](TSEMO_Example.m). To use the algorithm on your own functions simply copy the same format as the functions shown in the [test-function folder](/Test_functions/). The algorithm can be applied to any number of inputs and objectives. \n\n## Example applications\nThe algorithm has been successfully applied to several expensive multiobjective optimization problems:\n\n* Determination of optimal conditions of a fully-automated chemical reactor system trading-off yield and environmental factors [(Schweidtmann et al., 2018)](#Schweidtmann2018) including multi-step reactions and separation processes [(Clayton et al., 2020)](#Clayton2020)\n\n![](https://ars.els-cdn.com/content/image/1-s2.0-S1385894718312634-gr2.jpg)\n\n* Optimization of a chemical process using a life-cycle assessment and cost simulation [(Helmdach et al., 2018)](#Helmdach2017) \n\n* Solvent selection for asymmetric catalysis using molecular descriptors [(Amar et al., 2019)](#Amar2019)\n\n\n## References\nE. Bradford, A. M. Schweidtmann, and A. A. Lapkin, [Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm](https://link.springer.com/article/10.1007/s10898-018-0609-2/), Journal of Global Optimization, vol. 71, no. 2, pp. 407–438, 2018.\n\n\u003ca name=\"Bradford2018\"\u003e\n\u003c/a\u003e\n\nA. M. Schweidtmann, A. D. Clayton, N. Holmes, E. Bradford, R. A. Bourne, and A. A. Lapkin, [Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives](https://www.sciencedirect.com/science/article/pii/S1385894718312634), Chemical Engineering Journal, vol. 352, pp. 277-282, 2018.    \n\n\u003ca name=\"Schweidtmann2018\"\u003e\n\u003c/a\u003e\n\nD. Helmdach, P. Yaseneva, K. P. Heer, A. M. Schweidtmann, and A. A. Lapkin, [A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes](https://onlinelibrary.wiley.com/doi/abs/10.1002/cssc.201700927), ChemSusChem, vol. 10, no. 18, pp. 3632-3643, 2017.  \n\n\u003ca name=\"Helmdach2017\"\u003e\n\u003c/a\u003e\n\nY. Amar, A. M. Schweidtmann, P. Deutsch, L. Cao, and A. A. Lapkin, [Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis](https://pubs.rsc.org/en/content/articlelanding/2019/sc/c9sc01844a#!divAbstract), Chemical Science, vol. 10, no. 27, pp. 6697-6706, 2019. \n\u003ca name=\"Amar2019\"\u003e\n\u003c/a\u003e\n\nA. Clayton, A. M. Schweidtmann, G. Clemens, J. Manson, C. Taylor, C. Nino, T. Chamberlain, N. Kapur, A. Blacker, A. A. Lapkin, R. Bourne [Automated self-optimisation of multi-step reaction and separation processes using machine learning](https://doi.org/10.1016/j.cej.2019.123340), Chemical Engineering Journal, vol. 384, 123340, 2020. \n\u003ca name=\"Clayton2020\"\u003e\n\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feric-bradford%2Fts-emo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feric-bradford%2Fts-emo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feric-bradford%2Fts-emo/lists"}