swarmtorch 0.3.0

Professional PyTorch library for 120+ metaheuristic optimization algorithms (Swarm, Evolutionary, Physics, Hybrid).

SwarmTorch 🐝🔥

A PyTorch Library for Metaheuristic Optimization in Deep Learning

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📄 About

SwarmTorch is a comprehensive PyTorch library that brings 120 metaheuristic optimization algorithms to the deep learning ecosystem. It provides a unified API for:

• 60 model-training optimizers — gradient-free weight optimization for neural networks
• 60 hyperparameter searchers — intelligent hyperparameter optimization (HPO)

Published on arXiv: arXiv:2503.XXXXX

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🚀 Key Features

• 120 Algorithms Across 6 Families: Swarm Intelligence, Evolutionary, Physics-based, Human-based, Bio-inspired, and Hybrid methods
• Native PyTorch Integration: Drop-in replacement for torch.optim.Optimizer
• GPU-Accelerated: Full tensor parallelism using PyTorch's CUDA support
• Research-Grade Benchmarks: Comprehensive evaluation across 60 training algorithms and 60 HPO searchers with reproducible results

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📊 Key Research Findings

Our empirical evaluation on standard benchmarks demonstrates:

• 63.3% of SwarmTorch's hyperparameter searchers outperform Random Search baseline
• Metaheuristics achieve significantly lower loss on multimodal (non-convex) test functions where gradient-based methods fail
• Up to 17.4× GPU speedup for models with >50K parameters

See our paper for detailed results.

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📦 Installation

pip install swarmtorch

With development dependencies:

pip install -e ".[dev]"

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💻 Usage Examples

Model Weight Optimization

import torch.nn as nn
from swarmtorch import PSO

# Define model
model = nn.Sequential(
    nn.Linear(10, 64),
    nn.ReLU(),
    nn.Linear(64, 1)
)

# Use PSO as optimizer
optimizer = PSO(model.parameters(), swarm_size=30)

# Training loop
for epoch in range(100):
    def closure():
        optimizer.zero_grad()
        output = model(inputs)
        loss = criterion(output, targets)
        loss.backward()
        return loss
    
    optimizer.step(closure)

Hyperparameter Optimization

from swarmtorch import PSOSearch

# Define search space
searcher = PSOSearch(
    model_fn=build_model,
    param_space={
        'lr': (0.001, 0.1),
        'hidden_dim': [32, 64, 128],
        'batch_size': [16, 32, 64]
    },
    train_fn=train_fn,
    iterations=50,
    swarm_size=20
)

# Run optimization
best_params = searcher.search()

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📂 Repository Structure

swarmtorch/
├── swarmtorch/          # Main library
│   ├── swarm/          # Swarm Intelligence algorithms
│   ├── evolutionary/   # Evolutionary algorithms
│   ├── physics/       # Physics-based algorithms
│   ├── bio_inspired/  # Bio-inspired algorithms
│   ├── human_based/  # Human-based algorithms
│   └── hybrid/        # Hybrid algorithms
├── benchmarks/         # Experimental benchmarks
├── paper/             # Research paper & figures
└── README.md

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📚 Benchmark Results

Detailed experimental results are available in:

• COMPREHENSIVE_EXPERIMENT_REPORT.md
• BENCHMARKS.md

Top Performing HPO Searchers

Rank	Algorithm	Best Accuracy
1	SA Search	98.5%
2	DVBA Search	98.5%
3	PBIL Search	98.0%
4	AFSA Search	98.0%
5	JSO Search	98.0%

Top Performing Training Optimizers

Rank	Algorithm	Final Loss
1	Adam (baseline)	0.033
2	CA	0.132
3	HHO	0.167
4	CEM	0.199
5	PFA	0.239

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🔧 Hardware & Reproducibility

All benchmarks were run on:

• GPU: NVIDIA T4x2 (Kaggle)
• CPU: Intel Core i7-12700K (workstation)
• Software: PyTorch 2.9.0+cu126, Python 3.11, NumPy 1.26

Random seeds fixed at 42 for reproducibility.

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📝 Citation

If you use SwarmTorch in your research, please cite:

@article{swarmtorch2026,
  author  = {Halleluyah Darasimi Oludele},
  title   = {SwarmTorch: A PyTorch Library for 120 Metaheuristic Optimization Algorithms in Deep Learning},
  journal = {arXiv preprint arXiv:2503.XXXXX},
  year    = {2026}
}

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📄 License

MIT License - See LICENSE for details.

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🤝 Acknowledgments

Inspired by pyMetaheuristic and the broader metaheuristic optimization community.