The Borg Multiobjective Evolutionary Algorithm (MOEA) is a state-of-the-art optimization
algorithm developed by David Hadka and Patrick Reed at the Pennsylvania State University. Borg is freely
available for academic and non-commercial use. Use this site to learn more about the Borg MOEA and request
access to its source code.
Easy-to-use command line interface
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Borg efficiently captures the tradeoffs between many conflicting performance objectives,
providing decision makers with detailed insight into their problem characteristics.
Borg uses an ensemble of search operators, auto-adapting their use at runtime to tailor itself to your optimization
Written in efficient, high-performance ANSI C, the Borg MOEA wastes little time when
solving your problem. Runs on Unix, Linux, Windows, and Mac.
The Borg MOEA is freely available to academic and non-commercial users. Please complete the
below to request access to the source code. You will receive an e-mail within three business days giving
access to the Borg MOEA source code.
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- Hadka, D. and P. Reed. Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework. Evolutionary Computation, 21(2):231-259, 2013. View Details
- Hadka, D. and P. Reed. Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization. Evolutionary Computation, 20(3):423-452, 2012. View Details
- Reed, P., et al. Evolutionary Multiobjective Optimization in Water Resources: The Past, Present, and Future. (Editor Invited Submission to the 35th Anniversary Special Issue), Advances in Water Resources, 51:438-456, 2013. View Details
- Hadka, D. et al. Diagnostic Assessment of the Borg MOEA on Many-Objective Product Family Design Problems. WCCI 2012 World Congress on Computational Intelligence, Congress on Evolutionary Computation, Brisbane, Australia, 10-15 June 2012, pp. 986-995. View Details
- Hadka, D., et al. Scalability Analysis of the Asynchronous Master-Slave Borg Multiobjective Evolutionary Algorithm The 16th International Workshop on Nature Inspired Distributed Computing (NIDISC) at the 27th IEEE/ACM International Parallel and Distributed Processing Symposium (IPDPS), Boston, MA, 20 May 2013. View Details
- Hadka, D. and P. Reed. Large-scale Parallelization of the Borg Multiobjective Evolutionary Algorithm to Enhance the Management of Complex Environmental Systems. Modelling & Software, 69:353-369, 2014. View Details
- Reed, P. M. and D. Hadka. Evolving Many-Objective Water Management to Exploit Exascale Computing. Water Resources Research, 50(10):8367-8373, 2014. View Details
- Rangarajan, H., D. Hadka, P. Reed, and J. P. Lynch. Multi‐objective optimization of root phenotypes for nutrient capture using evolutionary algorithms. The Plant Journal 111 (1), 38-53, 2022. Download PDF
- Giuliani, M. Agent-Based Water Resources Management in Complex Decision-Making Contexts. Doctoral Dissertation, Politecnico di Milano, 2013. Download PDF
- d’Ervau, E. L. Optimizing Early-Warning Monitoring Systems for Improved Drinking Water Resource Protection. Master's Thesis, Universität Stuttgart, 8 October 2013. Download PDF
- Woodruff, M. et al. Many-Objective Visual Analytics: Rethinking the Design of Complex Engineered Systems. Structural and Multidisciplinary Optimization, 48:201-219, 2013. View Details
- Woodruff, M. et al. Auto-Adaptive Search Capabilities of the New Borg MOEA: A Detailed Comparison on Product Family Design Problems. 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Indianapolis, Indiana, 17 September 2012. View Details