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

Abstract

This research article introduces a hybrid optimization algorithm, referred to as Grey Wolf Optimizer-Teaching Learning Based Optimization (GWO-TLBO), which extends the Grey Wolf Optimizer (GWO) by integrating it with Teaching-Learning-Based Optimization (TLBO). The benefit of GWO is that it explores potential solutions in a way similar to how grey wolves hunt, but the challenge with this approach comes during fine-tuning, where the algorithm settles too early on suboptimal results. This weakness can be compensated by integrating TLBO method into the algorithm to improve its search power of solutions as in teaches students how to learn and teachers are knowledge felicitator. GWO-TLBO algorithm was applied for several benchmark optimization problems to evaluate its effectiveness in simple to complex scenarios. It is also faster, more accurate and reliable when compare to other existing optimization algorithms. This novel approach achieves a balance between exploration and exploitation, demonstrating adaptability in identifying new solutions but also quickly zoom in on (near) global optima: this renders it a reliable choice for challenging optimization problems according to the analysis and results.