New approach on global optimization problems based on meta-heuristic algorithm and quasi-Newton method

<p>This paper presents an innovative approach in finding optimal solution of multimodal and multivariable function for global optimization problems that involve complex inefficient second derivatives. Artificial bees colony (ABC) algorithm possessed good exploration search, but the major weakness at its exploitation stage. The proposed algorithms improved ABC by hybridized with most effective gradient based method which are Davidon-Flecher-Powell (DFP) Broyden-Flecher-Goldfarb-Shanno (BFGS) algorithms. Its distinguished features include maximizing employment possible information related to objective obtained previous iterations. have been tested on a large set benchmark it has shown satisfactory computational behaviour succeeded enhancing obtain problems.</p>