Somersaulting Spider Optimizer (SSO)

Somersaulting Spider Optimizer (SSO) is a metaheuristic inspired by the movement of Somersaulting Spiders.

Somersaulting Spider Optimizer (SSO) – MATLAB Version

The Somersaulting Spider Optimizer (SSO) is a novel metaheuristic optimization algorithm inspired by the dynamic movement strategies of somersaulting spiders. By simulating their ability to alternate between high-mobility somersaults and agile rolling behaviors, SSO provides a powerful framework for efficiently solving challenging optimization problems across academic and industrial domains.

This MATLAB version is fully modular, supporting continuous, discrete, and binary optimization tasks, making it suitable for a wide range of research and engineering applications.

📖 Description

SSO belongs to the family of nature-inspired metaheuristic algorithms. It alternates between two biologically inspired strategies:

• Somersaulting (Exploration): A global search mechanism that helps spiders escape local traps and discover new promising regions.
• Rolling (Exploitation): A local refinement process that improves solution quality by exploiting the best areas found so far.

This alternating search pattern ensures a balance between diversification and intensification, which is essential for solving complex, nonlinear, and high-dimensional problems.

✨ Features

• Somersaulting Movement → Enables global exploration and avoids premature convergence.
• Rolling Movement → Focuses on local exploitation and fine-tuning of candidate solutions.
• Adaptive Energy Mechanism → Dynamically adjusts agent behaviors to prevent stagnation.
• Broad Applicability → Works with continuous, discrete, and binary optimization tasks.
• Modular Design → Plug-and-play with custom objective functions.
• Benchmark-Ready → Easy integration with test functions for academic evaluation.

⚙️ How It Works

1. Initialization → Randomly generate a population of candidate solutions.
2. Evaluation → Compute the fitness of each candidate using the objective function.
3. Somersaulting & Rolling → Alternate between global and local search phases.
4. Adaptation → Update agents’ energy and stagnation levels to maintain balance.
5. Convergence → Stop when reaching the maximum iterations or a convergence criterion.

📚 Citation

If you use this MATLAB implementation of SSO in your research, please cite the original paper:

@article{zaki_somersaulting_2025,
title     = {Somersaulting Spider Optimizer (SSO): A Nature-Inspired Metaheuristic Algorithm for Engineering Optimization Problems},
    shorttitle= {Somersaulting Spider Optimizer (SSO)},
    url       = {https://www.americaspg.com/articleinfo/28/show/4115},
    doi       = {10.54216/JAIM.100105},
    journal   = {Journal of Artificial Intelligence and Metaheuristics},
    author    = {Zaki, Ahmed Mohamed and Nafea, Hala B. and Moustafa, Hossam El-Din and El-Kenawy, El-Sayed M.},
    year      = {2025},
    month     = jan,
    number    = {Issue 1},
    pages     = {91--120},
    publisher = {American Scientific Publishing Group (ASPG)}
    }

📌 Link to the paper: Somersaulting Spider Optimizer (SSO) – Journal Article

📄 License

This project is licensed under the MIT License.