  As an effective algorithm for solving complex optimization problems,
artificial bee colony (ABC) algorithm has shown to be competitive, but the same
as other population-based algorithms, it is poor at balancing the abilities of
global searching in the whole solution space (named as exploration) and quick
searching in local solution space which is defined as exploitation. For
improving the performance of ABC, an adaptive group collaborative ABC (AgABC)
algorithm is introduced where the population in different phases is divided to
specific groups and different search strategies with different abilities are
assigned to the members in groups, and the member or strategy which obtains the
best solution will be employed for further searching. Experimental results on
benchmark functions show that the proposed algorithm with dynamic mechanism is
superior to other algorithms in searching accuracy and stability. Furthermore,
numerical experiments show that the proposed method can generate the optimal
solution for the complex scheduling problem.

Neural and Evolutionary Computing;Machine Learning