A novel bio-inspired swarm intelligence optimizer
Modelling the immortal jellyfish lifecycle × lighthouse light propagation
The Immortal Jellyfish Algorithm (IJA) is a novel metaheuristic optimizer inspired by two natural phenomena:
- The lifecycle of Turritopsis dohrnii — the only known animal that can revert from adult medusa to juvenile polyp form, cycling through four life stages indefinitely.
- Turritopsis dohrnii light propagation — the inverse-square law governing how light intensity falls off with distance from a point source.
Available in Python, C++, and Java.
──────────────────────────────────────────────────────────────▶ τ = t / T_max
[0, 0.15) [0.15, 0.50) [0.50, 0.85) [0.85, 1]
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ POLYP │ -> │ STROBILATION │ -> │ MEDUSA │ -> │ SENESCENCE │
│ │ │ │ │ │ │ │
│ Lévy-flight │ │ Adaptive │ │ Lighthouse │ │ Gaussian │
│ exploration │ │ ephyra buds │ │ attraction │ │ refinement │
└──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘
Global search Diversify Converge Fine-tune
Key operators:
Full derivations: docs/algorithm_details.md
Experiments: 30 independent runs, dimension D = 30.
IJA uses recommended settings; competitors use standard/published settings.
pip install -r requirements.txt
python - <<'EOF'
import sys; sys.path.insert(0, "python")
from ija import IJA
import numpy as np
result = IJA(n=100, max_iter=2000, seed=0).optimize(
lambda x: np.sum(x**2), dim=30, lb=-100.0, ub=100.0
)
print(f"Best fitness : {result.best_fitness:.6e}")
print(f"NFev : {result.n_function_evals}")
EOFMore examples: python python/examples/quickstart.py
cd cpp && mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release && make -j4
./ija_democd java && mvn compile
mvn exec:java -Dexec.mainClass="ija.Main"immortal-jellyfish-algorithm/
├── python/ # PRIMARY IMPLEMENTATION
│ ├── ija/
│ │ ├── __init__.py # Public API
│ │ ├── algorithm.py # Main optimizer class
│ │ ├── operators.py # All mathematical operators
│ │ ├── lifecycle.py # Phase scheduler
│ │ └── archive.py # Crowding-distance elite archive
│ ├── benchmarks/functions.py # 8 benchmark functions
│ ├── compare/algorithms.py # PSO, DE, GWO, WOA, SCA
│ ├── experiments/
│ │ ├── run_all.py # 30 runs × 6 algos × 8 funcs
│ │ └── visualize.py # All figures
│ └── examples/quickstart.py
├── cpp/ # C++ (competitive-style)
├── java/ # Java (Builder pattern)
├── docs/algorithm_details.md
└── results/ # Generated figures & JSON
| Parameter | Recommended | Description |
|---|---|---|
n |
100 | Population size |
max_iter |
2000 | Maximum iterations |
top_k |
5 | Lighthouse elites |
levy_beta |
1.5 | Lévy exponent |
archive_size |
20 | Elite archive capacity |
age_max |
20 | Stagnation threshold |
decay_lambda |
0.3 | Intensity decay rate |
alpha_max |
2.5 | Max step-size |
alpha_min |
0.01 | Min step-size |
@software{ija2025,
title = {Immortal Jellyfish Algorithm (IJA)},
author = {IJA Contributors},
year = {2025},
url = {https://github.com/LangZhong36/immortal-jellyfish-algorithm},
license = {MIT}
}Fork → feature branch → PR. All three language implementations should remain feature-equivalent.
MIT — see LICENSE.