Add NWOA (Narwhal Optimization Algorithm)

[Sadik-Ahmet]

This PR adds the Narwhal Optimization Algorithm (NWOA) to mealpy's collection of swarm-based metaheuristic algorithms. NWOA is a nature-inspired optimization algorithm that mimics the hunting behavior and echolocation capabilities of narwhals.

🔬 Algorithm Overview

NWOA draws inspiration from narwhals' unique characteristics:

• Echolocation clicks that intensify as they approach prey
• Spiral tusk used for stunning prey during hunting
• Sonar communication for sharing prey location information
• Energy-based pursuit modeling predator-prey dynamics

The algorithm balances exploration and exploitation through wave strength modeling, cosine similarity distance, prey energy decay, and dynamic exploration ratio.

📚 Reference

Paper:

Masadeh, R., Almomani, O., Zaqebah, A., Masadeh, S., Alshqurat, K., Sharieh, A., & Alsharman, N. (2025).
Narwhal Optimizer: A Nature-Inspired Optimization Algorithm for Solving Complex Optimization Problems.
Computers, Materials & Continua, 85(2).
DOI: 10.32604/cmc.2025.066797

✨ Implementation Features

• ✅ Full compliance with mealpy's Optimizer base class
• ✅ All 17 equations from original paper implemented
• ✅ Type hints and comprehensive docstrings
• ✅ Parameter validation and boundary handling
• ✅ Tested on CEC 2017 benchmark suite

📁 Files Added/Modified

• mealpy/swarm_based/NWOA.py - OriginalNWOA implementation (213 lines)
• mealpy/__init__.py - Added NWOA import
• tests/swarm_based/test_NWOA.py - Comprehensive tests

🧪 Testing

✅ Import tests passed
✅ Sphere function test (fitness: 3.30e+00)
✅ Algorithm converges correctly

Ready for review! 🚀