🐋 Enhanced Whale Optimization Algorithm (EWOA)

An enhanced implementation of the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic optimization technique based on the bubble-net hunting strategy of humpback whales. This project introduces several improvements to the standard WOA to achieve faster convergence, better exploration, and improved solution quality on optimization problems.

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📌 Overview

The Whale Optimization Algorithm (WOA) is a population-based optimization algorithm proposed by Mirjalili and Lewis (2016). While the original WOA performs well on many optimization tasks, it may suffer from:

• Premature convergence
• Local optima stagnation
• Slow convergence on complex search spaces

This project implements an Enhanced Whale Optimization Algorithm (EWOA) with modifications designed to improve exploration and exploitation balance.

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✨ Features

• ✅ Enhanced exploration strategy
• ✅ Improved exploitation mechanism
• ✅ Faster convergence speed
• ✅ Reduced risk of local optima trapping
• ✅ Benchmark function evaluation
• ✅ Visualization of convergence curves
• ✅ Modular and extensible code structure

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🧠 Algorithm Workflow

1. Initialize whale population randomly.

2. Evaluate fitness of each whale.

3. Identify the current best solution.

4. Update whale positions using:

   • Encircling prey mechanism
   • Bubble-net attacking strategy
   • Random search exploration

5. Apply enhancement strategies.

6. Recalculate fitness values.

7. Update global best solution.

8. Repeat until termination criteria are met.

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⚙️ Installation

Clone Repository

git clone https://github.com/yourusername/Enhanced-Whale-Optimization-Algorithm.git

cd Enhanced-Whale-Optimization-Algorithm

Create Virtual Environment

python -m venv venv

Activate Environment

Windows:

venv\Scripts\activate

Linux/Mac:

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

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🚀 Usage

Run the optimization algorithm:

python main.py

Example:

from src.ewoa import EnhancedWOA

optimizer = EnhancedWOA(
    population_size=30,
    max_iterations=500,
    dimensions=30
)

best_solution, best_fitness = optimizer.optimize()

print(best_solution)
print(best_fitness)

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📊 Experimental Results

The Enhanced WOA was evaluated on standard benchmark optimization functions.

Function	WOA	Enhanced WOA
Sphere	Better	Best
Rastrigin	Better	Best
Rosenbrock	Better	Best
Ackley	Better	Best

Convergence Curve

Add your convergence plot here:

![Convergence Curve](plots/convergence.png)

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🔬 Enhancement Techniques

This implementation introduces:

• Adaptive parameter control
• Dynamic exploration-exploitation balancing
• Diversity preservation mechanism
• Improved position update strategy
• Random perturbation for escaping local optima

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📈 Applications

EWOA can be applied to:

• Machine Learning Hyperparameter Optimization
• Feature Selection
• Engineering Design Optimization
• Resource Allocation
• Scheduling Problems
• Energy Management Systems
• Image Processing Tasks

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🛠 Technologies Used

• Python
• NumPy
• Matplotlib
• Pandas
• Scikit-Learn

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📚 References

1. Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67.

2. Various recent enhancements and hybrid metaheuristic optimization studies.

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🤝 Contributing

Contributions are welcome.

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to your branch
5. Open a Pull Request

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author

Abdul Mujeeb

B.Tech Student | AI & Machine Learning Enthusiast

GitHub: https://github.com/yourusername