Surrogate Optimization Toolbox for Python

Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP

Framework of intelligent optimization methods iOpt

Global optimization algorithms written in C++

A settings-free global optimization method based on PSO and fuzzy logic

Feature Selection using Simulated Annealing

The python implementation of Partition-based Random Search for stochastic multi-objective optimization via simulation

Comparison of global optimization algorithms, including scipy, optuna, hyperopt, platypus, facebook-ax, pysot and others.

Multi-objective Simulated Annealing (MOSA) implementation in pure Python.

Testing optimizers and time-series algorithms, and assigning them Elo ratings.

Python3 code for gradient-free global optimization

A Complete Toolbox for Adaptive and Hybrid Genetic Algorithms in R

MABSearch: The Bandit Way of Learning the Learning Rate - A Harmony Between Reinforcement Learning and Gradient Descent

Python GOSOLNP implementation based on pysolnp. This algorithm solves Global Optimization problems with optional equality and/or inequality constraints.

C++ implementation of the metaheuristic spy_algorithm. It can search for global optimal solutions for arbitrary non-convex functions.

Sequential Minimal Optimization (SMO) algorithm for Standard Quadratic Problems (StQPs) - Master's thesis in Computer Science & Engineering @ UNIFI

Adaptive nested optimization scheme for Lipschitz continuous functions

Implementation of the Hybrid Patch Decomposition Algorithm - a solver for multi-objective mixed-integer convex optimization problems

Low-energy Latent Space Search Workflow.

A small utility project in Python whose purpose is to evaluate the global minimum of a given function, using a black box approach. Therefore there's no need to provide the analytic form, an actual implementation of the function is enough. Hypothesis: the given function satisfies the Lipschitz criterion, and an overextimation of L is given in input.