"""
Grey Wolf Optimizer (GWO)
This module implements the Grey Wolf Optimizer for single-objective optimization problems.
References
----------
[1] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
Notes
-----
Author: Jiangtao Shen
Email: j.shen5@exeter.ac.uk
Date: 2025.12.08
Version: 1.0
"""
import time
from tqdm import tqdm
import numpy as np
from ddmtolab.Methods.Algo_Methods.algo_utils import *
[docs]
class GWO:
"""
Grey Wolf Optimizer for single-objective optimization.
Attributes
----------
algorithm_information : dict
Dictionary containing algorithm capabilities and requirements
"""
algorithm_information = {
'n_tasks': '[1, K]',
'dims': 'unequal',
'objs': 'equal',
'n_objs': '1',
'cons': 'unequal',
'n_cons': '[0, C]',
'expensive': 'False',
'knowledge_transfer': 'False',
'n': 'unequal',
'max_nfes': 'unequal'
}
@classmethod
def get_algorithm_information(cls, print_info=True):
return get_algorithm_information(cls, print_info)
[docs]
def __init__(self, problem, n=None, max_nfes=None, save_data=True,
save_path='./Data', name='GWO', disable_tqdm=True):
"""
Initialize Grey Wolf Optimizer.
Parameters
----------
problem : MTOP
Multi-task optimization problem instance
n : int or List[int], optional
Population size per task (default: 100)
max_nfes : int or List[int], optional
Maximum number of function evaluations per task (default: 10000)
save_data : bool, optional
Whether to save optimization data (default: True)
save_path : str, optional
Path to save results (default: './Data')
name : str, optional
Name for the experiment (default: 'GWO')
disable_tqdm : bool, optional
Whether to disable progress bar (default: True)
"""
self.problem = problem
self.n = n if n is not None else 100
self.max_nfes = max_nfes if max_nfes is not None else 10000
self.save_data = save_data
self.save_path = save_path
self.name = name
self.disable_tqdm = disable_tqdm
[docs]
def optimize(self):
"""
Execute the Grey Wolf Optimizer algorithm.
Returns
-------
Results
Optimization results containing decision variables, objectives, and runtime
"""
start_time = time.time()
problem = self.problem
nt = problem.n_tasks
n_per_task = par_list(self.n, nt)
max_nfes_per_task = par_list(self.max_nfes, nt)
# Initialize population in [0,1] space and evaluate for each task
decs = initialization(problem, n_per_task)
objs, cons = evaluation(problem, decs)
nfes_per_task = n_per_task.copy()
all_decs, all_objs, all_cons = init_history(decs, objs, cons)
total_nfes = sum(max_nfes_per_task)
pbar = tqdm(total=total_nfes, initial=sum(n_per_task), desc=f"{self.name}",
disable=self.disable_tqdm)
while sum(nfes_per_task) < total_nfes:
# Skip tasks that have exhausted their evaluation budget
active_tasks = [i for i in range(nt) if nfes_per_task[i] < max_nfes_per_task[i]]
if not active_tasks:
break
for i in active_tasks:
n = n_per_task[i]
d = problem.dims[i]
# Rank current population (constraint violation first, then objective)
# and take the top-3 wolves as alpha, beta, delta for this generation
cvs = np.sum(np.maximum(0, cons[i]), axis=1)
order = np.lexsort((objs[i].flatten(), cvs))
alpha = decs[i][order[0]]
beta = decs[i][order[min(1, n - 1)]]
delta = decs[i][order[min(2, n - 1)]]
# Distances to leaders: D = |2*rand .* leader - X|
d_alpha = np.abs(2 * np.random.rand(n, d) * alpha - decs[i])
d_beta = np.abs(2 * np.random.rand(n, d) * beta - decs[i])
d_delta = np.abs(2 * np.random.rand(n, d) * delta - decs[i])
# Linearly decrease a from 2 to 0
a = 2 * (1 - nfes_per_task[i] / max_nfes_per_task[i])
# Leader-guided candidate positions: X_k = leader - A .* D, A in [-a, a]
x1 = alpha - (2 * a * np.random.rand(n, d) - a) * d_alpha
x2 = beta - (2 * a * np.random.rand(n, d) - a) * d_beta
x3 = delta - (2 * a * np.random.rand(n, d) - a) * d_delta
# New population: mean of the three leader-guided positions,
# full replacement without elitism, clipped to [0,1] space
decs[i] = np.clip((x1 + x2 + x3) / 3.0, 0, 1)
# Evaluate new positions (evaluation_single will transform to real space)
objs[i], cons[i] = evaluation_single(problem, decs[i], i)
nfes_per_task[i] += n_per_task[i]
pbar.update(n_per_task[i])
# Append current population to history
append_history(all_decs[i], decs[i], all_objs[i], objs[i], all_cons[i], cons[i])
pbar.close()
runtime = time.time() - start_time
# Save results
results = build_save_results(all_decs=all_decs, all_objs=all_objs, runtime=runtime, max_nfes=nfes_per_task,
all_cons=all_cons, bounds=problem.bounds, save_path=self.save_path,
filename=self.name, save_data=self.save_data)
return results