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View All on GitHub feat: Expand underpopulated optimization categories (social_inspired, probabilistic, constrained) about 1 hour ago
AI Summary: This GitHub issue aims to address the underpopulation of several optimization categories, specifically `social_inspired`, `probabilistic`, and `constrained`, which currently have significantly fewer algorithms than other categories. It highlights the current state of these categories and proposes adding new algorithms, such as Election Algorithm (EOA), Greedy Politics Optimization, and Social Learning Optimizer (SLO), to the `social_inspired` category to balance the library.
Complexity:
4/5
enhancement good first issue
A dedicated set of optimization algorithms for numeric problems.
Python
#math#optimization-algorithms#optimizer