"""
Competitive Swarm Optimizer (CSO)
This module implements Competitive Swarm Optimizer for single-objective optimization problems.
References
----------
[1] Cheng, Ran, and Yaochu Jin. "A competitive swarm optimizer for large scale optimization." IEEE Transactions on Cybernetics 45.2 (2015): 191-204.
Notes
-----
Author: Jiangtao Shen
Email: j.shen5@exeter.ac.uk
Date: 2025.10.31
Version: 1.0
"""
import time
import numpy as np
from tqdm import tqdm
from ddmtolab.Methods.Algo_Methods.algo_utils import *
[docs]
class CSO:
"""
Competitive Swarm Optimizer algorithm 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, phi=0.1, save_data=True, save_path='./Data', name='CSO',
disable_tqdm=True):
"""
Initialize Competitive Swarm Optimizer algorithm.
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)
phi : float, optional
Social influence parameter for mean position learning (default: 0.1)
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: 'CSO_test')
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.phi = phi
self.save_data = save_data
self.save_path = save_path
self.name = name
self.disable_tqdm = disable_tqdm
[docs]
def optimize(self):
"""
Execute the Competitive Swarm 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)
# Initialize particle velocities to zero
vel = [np.zeros_like(d) for d in decs]
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:
# Calculate constraint violations
cvs = np.sum(np.maximum(0, cons[i]), axis=1)
# Randomly pair particles for pairwise competition
rnd_idx = np.random.permutation(n_per_task[i])
loser_idx = rnd_idx[:n_per_task[i] // 2]
winner_idx = rnd_idx[n_per_task[i] // 2:]
# Determine actual winners and losers by comparing constraint violation first, then objectives
loser_objs = objs[i][loser_idx]
winner_objs = objs[i][winner_idx]
loser_cvs = cvs[loser_idx]
winner_cvs = cvs[winner_idx]
# Swap indices if loser is better than winner
# Better means: lower constraint violation, or same violation but lower objective
swap_mask = (loser_cvs < winner_cvs) | \
((loser_cvs == winner_cvs) & (loser_objs.flatten() < winner_objs.flatten()))
temp_idx = loser_idx[swap_mask].copy()
loser_idx[swap_mask] = winner_idx[swap_mask]
winner_idx[swap_mask] = temp_idx
# Calculate mean position of winners for social learning
winner_mean = np.mean(decs[i][winner_idx], axis=0, keepdims=True)
# Update each loser by learning from its paired winner and swarm mean
for j, loser_j in enumerate(loser_idx):
winner_j = winner_idx[j]
r1 = np.random.rand()
r2 = np.random.rand()
r3 = np.random.rand()
# Velocity update: inertia + learn from winner + learn from swarm mean
vel[i][loser_j] = (r1 * vel[i][loser_j] +
r2 * (decs[i][winner_j] - decs[i][loser_j]) +
self.phi * r3 * (winner_mean - decs[i][loser_j]))
# Update position and enforce boundary constraints: clip to [0,1] space
decs[i][loser_j] = np.clip(decs[i][loser_j] + vel[i][loser_j], 0, 1)
# Evaluate only updated losers (winners unchanged)
objs[i][loser_idx], cons[i][loser_idx] = evaluation_single(problem, decs[i][loser_idx], i)
nfes_per_task[i] += n_per_task[i] // 2
pbar.update(n_per_task[i] // 2)
# 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