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Makowski, M. 
ORCID: https://orcid.org/0000-0002-6107-0972, Granat, J., Shekhovtsov, A., Nahorski, Z., & Zhao, J.
(2025).
Version [2.0] — [pyMCMA: Uniformly distributed Pareto-front representation].
SoftwareX 30 e102097. 10.1016/j.softx.2025.102097.
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Abstract
pyMCMA is the Python implementation of a novel method for autonomous computation of the Pareto-front representation composed of efficient solutions distributed uniformly in terms of the distances between neighbor Pareto solutions. pyMCMA supports scientific, i.e., objective, model analysis by providing preference-free Pareto front representation.
The update provides new functionalities and enhancements. The former include clustering of the Pareto-front solutions. The enhancements include internal software improvements, optional customization of some parameters, as well as a new functionalities that might be used by advanced users.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Pareto-front representation, Multiple-criteria model analysis, Pareto-front visualization, Clustering, Pyomo modeling language, Structured modeling |
| Research Programs: | Energy, Climate, and Environment (ECE) Energy, Climate, and Environment (ECE) > Integrated Assessment and Climate Change (IACC) Energy, Climate, and Environment (ECE) > Sustainable Service Systems (S3) |
| Depositing User: | Luke Kirwan |
| Date Deposited: | 22 May 2025 08:23 |
| Last Modified: | 22 May 2025 08:23 |
| URI: | https://pure.iiasa.ac.at/20604 |
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