Improving the quality of the grey wolf algorithm and genetic algorithm through tornado local search

Abstract

This paper investigates the enhancement of the Grey Wolf Optimizer (GWO) by integrating it with the Genetic Algorithm (GA) and an additional tornado local search step to improve optimization performance. GWO is inspired by the hunting behavior of grey wolves, while GA is an evolutionary algorithm that simulates the process of natural selection. This hybrid approach leverages the exploitation capability of GWO, the exploration strength of GA, and the refinement potential of local search. Experiments on standard benchmark functions demonstrate that the GWO-GA approach significantly improves both accuracy and convergence speed compared to traditional methods. This novel approach to optimization promises applications across various fields. Future research includes optimizing algorithm parameters and testing on more complex, real-world optimization problems.