Grey Wolf Optimization (GWO)

Overview

Grey Wolf Optimization is a swarm intelligence metaheuristic inspired by the social hierarchy and hunting behaviour of grey wolves. It is an alternative population-based optimizer for continuous domains.

Quick facts

Type: Population-based swarm metaheuristic
Typical use: continuous search spaces where simple parameterization and few control parameters are desirable
Complexity: O(S D I) per iteration (similar to other swarm methods)

Core ideas

Simulate leadership ranks (alpha, beta, delta, omega) to guide search and exploitation.
Combine encircling, hunting and attacking behaviours to move candidate solutions towards promising regions.

Usage example (OptiFlowX)

from optiflowx.optimizers import GreyWolfOptimizer
from optiflowx.models.configs import RandomForestConfig
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
cfg = RandomForestConfig()
opt = GreyWolfOptimizer(search_space=cfg.build_search_space(), metric='accuracy', model_class=cfg.get_wrapper().model_class, X=X, y=y)
best, score = opt.run(max_iters=40)
print('best score', score)