Enhanced Grey Wolf Optimizer Algorithm (I-GWO)
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Resource Overview
The I-GWO algorithm incorporates an innovative movement strategy called Dimension Learning-Based Hunting (DLH), which mimics individual hunting behaviors observed in natural wolf packs. DLH constructs unique neighborhoods for each wolf using varied approaches, enabling information sharing among neighboring wolves. The dimension learning mechanism within DLH enhances the balance between local and global search capabilities while maintaining population diversity. The provided code demonstrates I-GWO implementation on benchmark test functions, featuring key components like position updating, fitness evaluation, and neighborhood construction functions.
Detailed Documentation
The Improved Grey Wolf Optimizer (I-GWO) algorithm delivers superior optimization results through its novel Dimension Learning-Based Hunting (DLH) movement strategy. Inspired by natural wolf hunting behaviors, DLH employs distinct methods to construct individualized neighborhoods for each wolf, facilitating information exchange between adjacent wolves. The dimension learning approach within DLH effectively balances local exploitation and global exploration while preserving population diversity. These characteristics make I-GWO particularly effective for benchmark function optimization. The accompanying MATLAB/Python code illustrates practical implementation with key functions handling: 1) Alpha, beta, and delta wolf position updates using social hierarchy simulation 2) Fitness calculation through objective function evaluation 3) DLH neighborhood generation using Euclidean distance metrics 4) Dynamic parameter adjustment for convergence control. Users can reference the code to understand parameter tuning and algorithm customization for specific optimization problems.
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