Skip to content

Replace multiprocessing with multiprocess. Fixes #480. #504

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
scimax wants to merge 9 commits into
base: master
Choose a base branch
from
Open

Replace multiprocessing with multiprocess. Fixes #480. #504

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
d5f0de2
Replace multiprocessing with multiprocess. Fixes #480.
scimax Jan 3, 2023
45ecbc5
pbcost default values set to inf
Oct 20, 2023
bdfcad5
New attributes added to class Swarm
Oct 20, 2023
cb23f66
modified local_best optimizer and example file
Oct 20, 2023
ff3f542
modified basic_optimization example file
Oct 20, 2023
4edfd37
Modified global_best.py to report best particle in the log
alipmv Jun 28, 2024
a62dc8c
improved initial position functionality
Apr 16, 2025
c624672
improved option handling in all optimizers
Jun 7, 2025
2431644
Merge pull request #1 from alipmv/master
scimax Oct 15, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
108 changes: 75 additions & 33 deletions docs/examples/tutorials/basic_optimization.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,17 +57,24 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2019-05-18 15:39:13,096 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=1.09e-41\n",
"2019-05-18 15:39:25,448 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1.093473857947962e-41, best pos: [3.27682830e-21 4.43998725e-22]\n"
"2023-10-20 20:31:20,814 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 0%| |0/1000, best_cost=0.299"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=7.27e-42\n",
"2023-10-20 20:31:22,219 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 7.274022421135793e-42, best pos: [-2.69536254e-21 9.50956183e-23]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.02 s, sys: 774 ms, total: 3.79 s\n",
"Wall time: 12.4 s\n"
"CPU times: user 1.42 s, sys: 210 ms, total: 1.63 s\n",
"Wall time: 1.41 s\n"
]
}
],
Expand All @@ -77,10 +84,10 @@
"options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}\n",
"\n",
"# Call instance of PSO\n",
"optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)\n",
"optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, max_generations=1000, options=options)\n",
"\n",
"# Perform optimization\n",
"cost, pos = optimizer.optimize(fx.sphere, iters=1000)"
"cost, pos = optimizer.optimize(fx.sphere)"
]
},
{
Expand All @@ -106,17 +113,24 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2019-05-18 15:39:25,476 - pyswarms.single.local_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}\n",
"pyswarms.single.local_best: 100%|██████████|1000/1000, best_cost=3.28e-41\n",
"2019-05-18 15:39:37,110 - pyswarms.single.local_best - INFO - Optimization finished | best cost: 3.275639739592901e-41, best pos: [-5.62944989e-21 -1.40094066e-21]\n"
"2023-10-20 20:31:22,272 - pyswarms.single.local_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}\n",
"pyswarms.single.local_best: 0%| |0/1000, best_cost=0.000431"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"pyswarms.single.local_best: 100%|██████████|1000/1000, best_cost=3.35e-44\n",
"2023-10-20 20:31:24,134 - pyswarms.single.local_best - INFO - Optimization finished | best cost: 3.348384631794686e-44, best pos: [-1.82343675e-22 1.53176551e-23]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.93 s, sys: 271 ms, total: 2.2 s\n",
"Wall time: 11.6 s\n"
"CPU times: user 1.88 s, sys: 270 ms, total: 2.15 s\n",
"Wall time: 1.87 s\n"
]
}
],
Expand All @@ -126,10 +140,10 @@
"options = {'c1': 0.5, 'c2': 0.3, 'w':0.9, 'k': 2, 'p': 2}\n",
"\n",
"# Call instance of PSO\n",
"optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, options=options)\n",
"optimizer = ps.single.LocalBestPSO(n_particles=10, dimensions=2, max_generations=1000, options=options)\n",
"\n",
"# Perform optimization\n",
"cost, pos = optimizer.optimize(fx.sphere, iters=1000)"
"cost, pos = optimizer.optimize(fx.sphere)"
]
},
{
Expand Down Expand Up @@ -179,17 +193,24 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2019-05-18 15:39:37,279 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=0 \n",
"2019-05-18 15:39:48,976 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0, best pos: [-1.11729550e-09 3.10827139e-09]\n"
"2023-10-20 20:31:24,200 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 0%| |0/1000, best_cost=3.66"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=0.995\n",
"2023-10-20 20:31:25,592 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.9949590570932898, best pos: [1.13585631e-10 9.94958639e-01]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.95 s, sys: 254 ms, total: 2.21 s\n",
"Wall time: 11.7 s\n"
"CPU times: user 1.41 s, sys: 197 ms, total: 1.61 s\n",
"Wall time: 1.4 s\n"
]
}
],
Expand All @@ -199,10 +220,10 @@
"options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}\n",
"\n",
"# Call instance of PSO with bounds argument\n",
"optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options, bounds=bounds)\n",
"optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, max_generations=1000, options=options, bounds=bounds)\n",
"\n",
"# Perform optimization\n",
"cost, pos = optimizer.optimize(fx.rastrigin, iters=1000)"
"cost, pos = optimizer.optimize(fx.rastrigin)"
]
},
{
Expand Down Expand Up @@ -257,9 +278,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2019-05-18 15:39:49,204 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=7.02e-10\n",
"2019-05-18 15:40:01,463 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 7.019703679797182e-10, best pos: [1.0000264 1.00005302]\n"
"2023-10-20 20:31:25,663 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 0%| |0/1000, best_cost=0.958"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=5.83e-15\n",
"2023-10-20 20:31:27,268 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 5.829665734825736e-15, best pos: [0.99999992 0.99999985]\n"
]
}
],
Expand All @@ -271,11 +299,11 @@
"x_min = -1 * x_max\n",
"bounds = (x_min, x_max)\n",
"options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"optimizer = GlobalBestPSO(n_particles=10, dimensions=2, options=options, bounds=bounds)\n",
"optimizer = GlobalBestPSO(n_particles=10, dimensions=2, max_generations=1000, options=options, bounds=bounds)\n",
"\n",
"# now run the optimization, pass a=1 and b=100 as a tuple assigned to args\n",
"\n",
"cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, a=1, b=100, c=0)"
"cost, pos = optimizer.optimize(rosenbrock_with_args, a=1, b=100, c=0)"
]
},
{
Expand All @@ -294,15 +322,22 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2019-05-18 15:40:01,475 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"2023-10-20 20:31:27,296 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 0%| |0/1000, best_cost=1.35e-16"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=0 \n",
"2019-05-18 15:40:13,805 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0, best pos: [1. 1.]\n"
"2023-10-20 20:31:28,579 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0, best pos: [1. 1.]\n"
]
}
],
"source": [
"kwargs={\"a\": 1.0, \"b\": 100.0, 'c':0}\n",
"cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, **kwargs)"
"cost, pos = optimizer.optimize(rosenbrock_with_args, **kwargs)"
]
},
{
Expand All @@ -321,14 +356,21 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2019-05-18 15:40:13,819 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"2023-10-20 20:31:28,625 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
"pyswarms.single.global_best: 0%| |0/1000, best_cost=0"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"pyswarms.single.global_best: 100%|██████████|1000/1000, best_cost=0\n",
"2019-05-18 15:40:25,963 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0, best pos: [1. 1.]\n"
"2023-10-20 20:31:30,090 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0, best pos: [1. 1.]\n"
]
}
],
"source": [
"cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, a=1, b=100)"
"cost, pos = optimizer.optimize(rosenbrock_with_args, a=1, b=100)"
]
}
],
Expand All @@ -349,7 +391,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.11.5"
}
},
"nbformat": 4,
Expand Down
Loading