An improved optimization method based on krill herd and artificial bee colony with information exchange

This study presents a robust optimization algorithm based on hybridization of krill herd (KH) and artificial bee colony (ABC) methods and the information exchange concept. The global optimal solutions found by the proposed hybrid KH and ABC (KHABC) algorithm are considered as a neighbor food source for onlooker bees in ABC. Thereafter, a local search is performed by the onlooker bees in order to find a better solution around the given neighbor food source. Both the methods—the KH and ABC—share the globally best solutions through the information exchange process between the krill and bees. Based on the results, the exchange process significantly improves exploration and exploitation of the hybrid method. Besides, a focused elitism scheme is introduced to enhance the performance of the developed algorithm. The validity of the KHABC method is verified using thirteen unconstrained benchmark functions, twenty-one CEC 2017 constrained real-parameter optimization problems, and ten CEC 2011 real world problems. The proposed method clearly demonstrates its ability to be a competitive optimization tool towards solving benchmark functions and real world problems.