Finding Global Optimal Solution by a Robust Quadratic Approximation Hybridized Genetic Algorithm

The present study proposes a novel approach for finding the global optimal solution in complex optimization problems. The proposed method combines a robust quadratic approximation technique with a genetic algorithm to enhance the efficiency and accuracy. Extensive experiments on a set of benchmark optimization problems have been used to demonstrate the fact that the hybridized genetic algorithm reliably beats traditional genetic algorithm. The main idea is to avoid getting trapped into local optima during simulation. The ability to overcome local optima and find the global optimal solution offers opportunities for a wide range of applications. Further, we study the application of this method in real world problems and look into its scalability and robustness by comparing the results of genetic algorithm (GA), hybrid genetic algorithm (GA-Q) and hybrid genetic firefly algorithm (H-GA-FA).