"""
Multi-Task Bayesian Optimization (MTBO)
This module implements MTBO for expensive multi-task optimization with knowledge transfer via multi-task Gaussian
processes.
References
----------
[1] Swersky, Kevin, Jasper Snoek, and Ryan P. Adams. "Multi-task bayesian optimization." Advances in neural information processing systems 26 (2013).
Notes
-----
Author: Jiangtao Shen
Email: j.shen5@exeter.ac.uk
Date: 2025.11.12
Version: 1.0
"""
from tqdm import tqdm
import torch
import time
from ddmtolab.Methods.Algo_Methods.algo_utils import *
from ddmtolab.Methods.Algo_Methods.bo_utils import mtgp_build, mtbo_next_point
import warnings
warnings.filterwarnings("ignore")
[docs]
class MTBO:
"""
Multi-Task Bayesian Optimization for expensive multi-task optimization problems.
Attributes
----------
algorithm_information : dict
Dictionary containing algorithm capabilities and requirements
"""
algorithm_information = {
'n_tasks': '[2, K]',
'dims': 'unequal',
'objs': 'equal',
'n_objs': '1',
'cons': 'equal',
'n_cons': '0',
'expensive': 'True',
'knowledge_transfer': 'True',
'n_initial': '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_initial=None, max_nfes=None, save_data=True, save_path='./Data', name='MTBO',
disable_tqdm=True):
"""
Initialize Multi-Task Bayesian Optimization algorithm.
Parameters
----------
problem : MTOP
Multi-task optimization problem instance
n_initial : int or List[int], optional
Number of initial samples per task (default: 50)
max_nfes : int or List[int], optional
Maximum number of function evaluations per task (default: 100)
save_data : bool, optional
Whether to save optimization data (default: True)
save_path : str, optional
Path to save results (default: './TestData')
name : str, optional
Name for the experiment (default: 'MTBO_test')
disable_tqdm : bool, optional
Whether to disable progress bar (default: True)
"""
self.problem = problem
self.n_initial = n_initial if n_initial is not None else 50
self.max_nfes = max_nfes if max_nfes is not None else 100
self.save_data = save_data
self.save_path = save_path
self.name = name
self.disable_tqdm = disable_tqdm
[docs]
def optimize(self):
"""
Execute the Multi-Task Bayesian Optimization algorithm.
Returns
-------
Results
Optimization results containing decision variables, objectives, and runtime
"""
data_type = torch.double
start_time = time.time()
problem = self.problem
nt = problem.n_tasks
dims = problem.dims
n_initial_per_task = par_list(self.n_initial, nt)
max_nfes_per_task = par_list(self.max_nfes, nt)
# Initialize samples using Latin Hypercube Sampling
decs = initialization(problem, self.n_initial, method='lhs')
objs, _ = evaluation(problem, decs)
nfes_per_task = n_initial_per_task.copy()
pbar = tqdm(total=sum(max_nfes_per_task), initial=sum(n_initial_per_task), desc=f"{self.name}",
disable=self.disable_tqdm)
while sum(nfes_per_task) < sum(max_nfes_per_task):
# 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
# Build multi-task Gaussian process surrogate model with normalized objectives
objs_normalized, _, _ = normalize(objs, axis=0, method='minmax')
mtgp = mtgp_build(decs, objs_normalized, dims, data_type=data_type)
for i in active_tasks:
# Select next sample point via acquisition function optimization
candidate_np = mtbo_next_point(mtgp=mtgp, task_id=i, objs=objs_normalized, dims=dims, nt=nt,
data_type=data_type)
obj, _ = evaluation_single(problem, candidate_np, i)
decs[i], objs[i] = vstack_groups((decs[i], candidate_np), (objs[i], obj))
nfes_per_task[i] += 1
pbar.update(1)
pbar.close()
runtime = time.time() - start_time
all_decs, all_objs = build_staircase_history(decs, objs, k=1)
# Save results
results = build_save_results(all_decs=all_decs, all_objs=all_objs, runtime=runtime, max_nfes=nfes_per_task,
bounds=problem.bounds, save_path=self.save_path,
filename=self.name, save_data=self.save_data)
return results