Differential artificial bee colony algorithm for global numerical optimization

Artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. In order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions, a differential ABC (DABC) is proposed. The differential operator obeys uniform distribution and creates candidate food position that can fully represent the solution space. So the diversity of populations and capability of global search will be enhanced. To show the performance of our proposed DABC, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the DABC can effectively enhance the searching efficiency and greatly improve the searching quality.