Taxonomy of Optimizers

Opytimizer organizes algorithms into distinct categories based on their underlying inspiration and behavior:

• Swarm-Based: Algorithms modeled after collective intelligence (e.g., Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony).
• Evolutionary: Algorithms based on natural selection and genetic operators (e.g., Genetic Algorithms, Differential Evolution, Genetic Programming).
• Science / Physics-Based: Algorithms inspired by physical laws, chemical processes, or mathematical theories (e.g., Simulated Annealing, Gravitational Search Algorithm, Harmony Search).
• Multi-Objective: Algorithms designed to optimize multiple conflicting objectives concurrently (e.g., NSGA-II, MOEA/D).