📄️ Description
Particle Swarm Optimization (PSO) is a computational method used for global optimization problems by iteratively improving a candidate solution with regard to a given measure of quality. It is inspired by social behavior in animals, ex. flocks of birds and schools of fish, and is particularly effective for problems with large and complex search spaces, and does not require differentiability.
📄️ Usage
Import
📄️ Examples
McCormick Function
📄️ References
Bonyadi, Mohammad Reza, and Zbigniew Michalewicz. 2017. “Particle Swarm Optimization for Single Objective Continuous Space Problems 1–54. https://doi.org/10.1162/evcor00180.