An integrative TLBO-driven hybrid grey wolf optimizer for the efficient resolution of multi-dimensional, nonlinear...

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization
• Algorithm Design

Background:

• Grey Wolf Optimizer (GWO) excels at exploring solutions but struggles with premature convergence to suboptimal results during fine-tuning.
• Teaching-Learning-Based Optimization (TLBO) effectively improves search capabilities by simulating educational processes.
• Hybridization is a key strategy to overcome limitations of individual metaheuristic algorithms.

Purpose of the Study:

• To introduce and evaluate a novel hybrid optimization algorithm, GWO-TLBO.
• To address the fine-tuning weaknesses of the Grey Wolf Optimizer by integrating Teaching-Learning-Based Optimization.
• To enhance the overall search power and convergence accuracy of optimization algorithms.

Main Methods:

• Development of the Grey Wolf Optimizer-Teaching Learning Based Optimization (GWO-TLBO) hybrid algorithm.
• Integration of Teaching-Learning-Based Optimization (TLBO) principles into the Grey Wolf Optimizer (GWO) framework.
• Application and testing of the GWO-TLBO algorithm on various benchmark optimization problems.

Main Results:

• The GWO-TLBO algorithm demonstrated superior performance across benchmark optimization problems of varying complexity.
• Comparative analysis showed GWO-TLBO to be faster, more accurate, and more reliable than existing optimization algorithms.
• The hybrid approach effectively balanced exploration and exploitation, improving the identification of near-global optima.

Conclusions:

• GWO-TLBO offers a robust and reliable solution for challenging optimization tasks.
• The integration of TLBO significantly enhances the exploitation and fine-tuning capabilities of GWO.
• This novel hybrid algorithm presents a promising advancement in the field of metaheuristic optimization.