There are 2 repositories under benchmark-functions topic.
Single- and Multi-Objective Optimization Test Functions
A Fortran 95 code for Particle Swarm Optimization. The code is general. The fitness function is defined in a separate file and can be replaced by any user defined fitness function.
A collection of the most commonly used Optimisation Algorithms for Data Science & Machine Learning
A set of common benchmark functions for testing optimization algorithms in Julia
This repository is used to implement and analyze nature inspired computing algorithms on various benchmark function. We also try to solve some real world problems by MHA.
This repository contains the standard Particle Swarm Optimization code (Matlab M-file) for optimizing the benchmark function.
**optiGTest** is a MATLAB's toolbox which regroups many existing test functions used for studying the performance of approximation techniques and optimization strategy. In particular, gradient of the test functions are provided.
A study on swarm intelligence optimizing neural networks for workload elasticity prediction
Javascript implementations of some of the main metaheuristic algorithms for bound constraint single objective continuous optimization problems.
A pure-MATLAB library of EVolutionary (population-based) OPTimization for Large-Scale black-box continuous Optimization (evopt-lso).
Optimization environment for TimeNET
This repository contains the Harris Hawks Optimization code (matlab M-file) for optimizing the benchmark function.
Benchmark functions to test optimisation algorithms.
Benchmark functions for validating optimization algorithm in C++
A set of Jupyter notebooks that investigate and compare the performance of several numerical optimization techniques, both unconstrained (univariate search, Powell's method and Gradient Descent (fixed step and optimal step)) and constrained (Exterior Penalty method).