Artificial bee colony (ABC) algorithm is a new global stochastic optimization algorithm
which mimics the intelligent behavior of honeybee swarms.It has been used to solve various optimization problems successfully.In order to further improve the performance of artificial bee colony algorithm
a mind evolutionary artificial bee colony algorithm (MEABC) based on the idea of mind evolutionary is proposed.Two strategies based on opposition learning and dimension updating are applied to MEABC algorithm
and the convergence of the MEABC algorithm is analyzed.Experimental results on four benchmark functions show that the MEABC algorithm can effectively avoid the premature convergence
greatly enhance the global optimization ability and improve the convergence speed.