Paper Title
Gradient Based Artificial Bee Colony Algorithm

In this paper, by integrating the artificial bee colony (ABC) with the gradient-based sequential quadratic programming (SQP), a new hybrid optimization method referred to as the GABC is introduced. The new algorithm combines the global exploration ability of the ABC to converge rapidly to a near optimum solution, and the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of well-known benchmark optimization problems is used to validate the performance of the GABC as a global optimization algorithm and facilitate comparison with the classical ABC. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions. Keywords— Hybrid Algorithm, Artificial Bee Colony, Sequential Quadratic Programming, Global Optimization.