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A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' doi:10.1016/j.ejor.2006.08.008 'NSGA-III' doi:10.1109/TEVC.2013.2281535 'MO-CMA-ES' doi:10.1145/1830483.1830573 The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) doi:10.1007/1-84628-137-7_6 and 'WFG4'-'WFG9' from Huband, et al. (2005) doi:10.1109/TEVC.2005.861417.
| Citation | MaOEA citation info |
| github.com/dots26/MaOEA | |
| System requirements | Python 3.x with following modules: PyGMO, NumPy, and cloudpickle |
| Bug report | File report |
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