[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-80829":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":14,"subscribersCount":14,"size":14,"stars1d":15,"stars7d":16,"stars30d":17,"stars90d":14,"forks30d":14,"starsTrendScore":18,"compositeScore":19,"rankGlobal":9,"rankLanguage":9,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":14,"starSnapshotCount":14,"syncStatus":28,"lastSyncTime":29,"discoverSource":30},80829,"immortal-jellyfish-algorithm","LangZhong36\u002Fimmortal-jellyfish-algorithm","LangZhong36","A novel bio-inspired swarm intelligence optimizer — immortal Jellyfish Algorithm (IJA)",null,"Python",118,17,12,0,7,40,77,29,85.39,"MIT License",false,"main",true,[],"2026-06-12 04:01:30","\u003Cdiv align=\"center\">\n\n# 🪼 Immortal Jellyfish Algorithm (IJA)\n\n**A novel bio-inspired swarm intelligence optimizer**  \n*Modelling the immortal jellyfish lifecycle × lighthouse light propagation*\n\n[![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-yellow.svg)](LICENSE)\n[![Python 3.8+](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.8%2B-3776AB?logo=python&logoColor=white)](https:\u002F\u002Fwww.python.org\u002F)\n[![C++17](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FC%2B%2B-17-00599C?logo=cplusplus&logoColor=white)](https:\u002F\u002Fen.cppreference.com\u002F)\n[![Java 8+](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FJava-8%2B-ED8B00?logo=openjdk&logoColor=white)](https:\u002F\u002Fwww.java.com\u002F)\n[![Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FLangZhong36\u002Fimmortal-jellyfish-algorithm?style=social)](https:\u002F\u002Fgithub.com\u002FLangZhong36\u002Fimmortal-jellyfish-algorithm)\n\n**[Chinese README](README_zh.md)**\n\n\u003C\u002Fdiv>\n\n---\n\n## 🌊 What is IJA?\n\nThe **Immortal Jellyfish Algorithm (IJA)** is a novel metaheuristic optimizer inspired by two natural phenomena:\n\n1. **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.\n2. **Turritopsis dohrnii light propagation** — the inverse-square law governing how light intensity falls off with distance from a point source.\n\nAvailable in **Python**, **C++**, and **Java**.\n\n---\n\n## 🔬 Four-Phase Lifecycle\n\n```\n ──────────────────────────────────────────────────────────────▶  τ = t \u002F T_max\n  [0, 0.15)              [0.15, 0.50)        [0.50, 0.85)         [0.85, 1]\n ┌──────────────┐      ┌──────────────┐    ┌──────────────┐    ┌──────────────┐\n │   POLYP      │  ->  │ STROBILATION │ -> │    MEDUSA    │ -> │  SENESCENCE  │\n │              │      │              │    │              │    │              │\n │ Lévy-flight  │      │ Adaptive     │    │ Lighthouse   │    │ Gaussian     │\n │ exploration  │      │ ephyra buds  │    │ attraction   │    │ refinement   │\n └──────────────┘      └──────────────┘    └──────────────┘    └──────────────┘\n   Global search          Diversify           Converge            Fine-tune\n```\n\n**Key operators:**\n\n$$I_{i,s} = \\frac{I_0}{1+\\kappa\\|\\mathbf{x}_i-\\mathbf{g}_s\\|^2}\\,e^{-\\lambda\\tau} \\qquad \\text{(Lighthouse Attraction)}$$\n\n$$\\mathbf{x}_i^{t+1} = \\mathbf{x}_i^t + 0.01(u-l)\\cdot L_j(\\beta)\\cdot(\\mathbf{x}_i^t - \\mathbf{x}^*) \\qquad \\text{(Lévy Exploration)}$$\n\n$$\\delta_j^t = A_0(1-\\tau)^2\\sin(2\\pi f_p\\tau+\\phi_i)(u_j-l_j) \\qquad \\text{(Pulsed Swimming)}$$\n\nFull derivations: [`docs\u002Falgorithm_details.md`](docs\u002Falgorithm_details.md)\n\n---\n\n## 📊 Benchmark Results\n\nExperiments: **30 independent runs**, dimension **D = 30**.  \nIJA uses *recommended* settings; competitors use *standard\u002Fpublished* settings.\n\n### Convergence Curves\n\n![Convergence Curves](results\u002Fconvergence_curves.png)\n\n### Algorithm Ranking Heatmap\n\n![Rank Heatmap](results\u002Frank_heatmap.png)\n\n### Average Rank Summary\n\n![Average Rank](results\u002Faverage_rank_bar.png)\n\n### Final Fitness Distribution\n\n![Boxplots](results\u002Fboxplots.png)\n\n---\n\n## 🚀 Quick Start\n\n### Python\n\n```bash\npip install -r requirements.txt\n\npython - \u003C\u003C'EOF'\nimport sys; sys.path.insert(0, \"python\")\nfrom ija import IJA\nimport numpy as np\n\nresult = IJA(n=100, max_iter=2000, seed=0).optimize(\n    lambda x: np.sum(x**2), dim=30, lb=-100.0, ub=100.0\n)\nprint(f\"Best fitness : {result.best_fitness:.6e}\")\nprint(f\"NFev         : {result.n_function_evals}\")\nEOF\n```\n\nMore examples: `python python\u002Fexamples\u002Fquickstart.py`\n\n### C++\n\n```bash\ncd cpp && mkdir build && cd build\ncmake .. -DCMAKE_BUILD_TYPE=Release && make -j4\n.\u002Fija_demo\n```\n\n### Java\n\n```bash\ncd java && mvn compile\nmvn exec:java -Dexec.mainClass=\"ija.Main\"\n```\n\n---\n\n## 📁 Project Structure\n\n```\nimmortal-jellyfish-algorithm\u002F\n├── python\u002F                        # PRIMARY IMPLEMENTATION\n│   ├── ija\u002F\n│   │   ├── __init__.py            # Public API\n│   │   ├── algorithm.py           # Main optimizer class\n│   │   ├── operators.py           # All mathematical operators\n│   │   ├── lifecycle.py           # Phase scheduler\n│   │   └── archive.py             # Crowding-distance elite archive\n│   ├── benchmarks\u002Ffunctions.py    # 8 benchmark functions\n│   ├── compare\u002Falgorithms.py      # PSO, DE, GWO, WOA, SCA\n│   ├── experiments\u002F\n│   │   ├── run_all.py             # 30 runs × 6 algos × 8 funcs\n│   │   └── visualize.py           # All figures\n│   └── examples\u002Fquickstart.py\n├── cpp\u002F                           # C++ (competitive-style)\n├── java\u002F                          # Java (Builder pattern)\n├── docs\u002Falgorithm_details.md\n└── results\u002F                       # Generated figures & JSON\n```\n\n---\n\n## ⚙️ IJA Recommended Parameters\n\n| Parameter | Recommended | Description |\n|-----------|:-----------:|-------------|\n| `n` | **100** | Population size |\n| `max_iter` | **2000** | Maximum iterations |\n| `top_k` | 5 | Lighthouse elites |\n| `levy_beta` | 1.5 | Lévy exponent |\n| `archive_size` | 20 | Elite archive capacity |\n| `age_max` | 20 | Stagnation threshold |\n| `decay_lambda` | 0.3 | Intensity decay rate |\n| `alpha_max` | 2.5 | Max step-size |\n| `alpha_min` | 0.01 | Min step-size |\n\n---\n\n## 📖 Citation\n\n```bibtex\n@software{ija2025,\n  title   = {Immortal Jellyfish Algorithm (IJA)},\n  author  = {IJA Contributors},\n  year    = {2025},\n  url     = {https:\u002F\u002Fgithub.com\u002FLangZhong36\u002Fimmortal-jellyfish-algorithm},\n  license = {MIT}\n}\n```\n\n---\n\n## 🤝 Contributing\n\nFork → feature branch → PR. All three language implementations should remain feature-equivalent.\n\n---\n\n## 📜 License\n\nMIT — see [LICENSE](LICENSE).\n\n---\n\n\u003Cdiv align=\"center\">\n⭐ If you find this project useful, please give it a star!\n\u003C\u002Fdiv>\n","Immortal Jellyfish Algorithm (IJA) 是一种新颖的生物启发式群体智能优化算法。该算法模仿了灯塔水母（Turritopsis dohrnii）的生命周期和光传播特性，通过四个阶段（幼体、出芽、成体和衰老）实现全局搜索、多样化、收敛和微调。IJA 核心功能包括Lévy飞行探索、自适应出芽、灯塔吸引和高斯细化等关键操作符。该算法使用Python编写，并支持C++和Java版本。适用于需要高效解决复杂优化问题的场景，如工程设计、经济建模和机器学习等领域。",2,"2026-06-11 04:02:29","CREATED_QUERY"]