v3.0.3

General Updates

• Updated project configuration files: setup.py, requirements.txt, and MANIFEST.in.
• Restructured README for better clarity.
• Updated examples and docs with new usage guides.
• Changed license from GPL GNU v3 to MIT.

Bug Fixes

• Fixed n_workers bug in the Optimizer class.
• Fixed bug in parallel mode when passing starting_positions in Optimizer.
• Fixed incorrect handling of fitness as position in AO class.

New Features

1. New Algorithm Categories

• sota_based group: A new category for state-of-the-art algorithms from CEC competitions.
  • Added LSHADEcnEpSin (OriginalLSHADEcnEpSin class).
  • Added IMOTE (OriginalIMOTE class).

2. New Modules

• Added chaotic module.
• Added fuzzy module.

3. Swarm-Based Algorithms

• Grey Wolf Optimizer (GWO) module: Added multiple variants:
  • GWO_WOA, IGWO, ChaoticGWO, FuzzyGWO, IncrementalGWO, ExGWO,
    DS_GWO, IOBL_GWO, OGWO, ER_GWO, CG_GWO.
• Aquila Optimizer (AO) module:
  • Added Adaptive Aquila Optimizer (AAO).
• Added new standalone algorithms:
  • Emperor Penguins Colony (EPC).
  • Spider Monkey Optimization (SMO).
  • Squirrel Search Algorithm (SquirrelSA).
  • Fitness Dependent Optimizer (FDO) (FDO module).

4. Physics-Based Algorithms

• Added Electrical Storm Optimization (ESO) module with OriginalESO class.

5. Human-Based Algorithms

• Added Ali Baba and the Forty Thieves (AFT) (AFT module).
• Added Child Drawing Development Optimization (CCDO) (CCDO module).

✅ This release focuses on:

• Expanding algorithm coverage (swarm-based, physics-based, human-based, and sota-based groups).
• Improving documentation and examples.
• Fixing critical bugs in the optimizer core.
• Transitioning license to MIT for broader usage.