New cooperative and modified variants of the migrating birds optimization algorithm

Migrating birds optimization algorithm (MBO) is a recently introduced nature inspired metaheuristic neighbourhood search approach and simulates V flight formation of migrating birds, which is an effective formation for birds in order to save the energy. Artificial bee colony (ABC) algorithm which is inspired by the bees' foraging behaviour is another powerful optimization algorithm. In this paper, two new variants of MBO algorithm are proposed and a set of performance tests are applied by using benchmark functions. Finally, the proposed methods are employed to train the neural networks which are implemented for nine different data sets in UCI and KEEL web sites. Results show that the proposed methods outperform the original version by performing good convergences to the global optimums.