These are standard optimization techniques that aims to find the optima of a single objective function.
Single-objective optimization where the search-space is continuous. Perfect for optimizing various functions.
pyswarms.single.global_best- classic global-best Particle Swarm Optimization algorithm with a star-topology. Every particle compares itself with the best-performing particle in the swarm.
pyswarms.single.local_best- classic local-best Particle Swarm Optimization algorithm with a ring-topology. Every particle compares itself only with its nearest-neighbours as computed by a distance metric.
Single-objective optimization where the search-space is discrete. Useful for job-scheduling, traveling salesman, or any other sequence-based problems.
pyswarms.discrete.binary- classic binary Particle Swarm Optimization algorithm without mutation. Uses a ring topology to choose its neighbours (but can be set to global).
These functions can be used as benchmark tests for assessing the performance of the optimization algorithm.
pyswarms.utils.functions.single_obj- single-objective test functions
These search methods can be used to compare the relative performance of hyperparameter value combinations in reducing a specified objective function.
pyswarms.utils.search.grid_search- exhaustive search of optimal performance on selected objective function over cartesian products of provided hyperparameter values
pyswarms.utils.search.random_search- search for optimal performance on selected objective function over combinations of randomly selected hyperparameter values within specified bounds for specified number of selection iterations