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Publikováno v:
PLoS ONE, Vol 15, Iss 6, p e0234625 (2020)
In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the i
Externí odkaz:
https://doaj.org/article/3e738e31675141b3a3a97fa871efb5c8
Publikováno v:
PLoS ONE, Vol 15, Iss 6, p e0234625 (2020)
PLoS ONE
PLoS ONE
In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b489ae14affc3d3ad00edbbf53fa7b8
https://doaj.org/article/3e738e31675141b3a3a97fa871efb5c8
https://doaj.org/article/3e738e31675141b3a3a97fa871efb5c8