LAUSR

Collective foraging and information sharing behaviors of honey bees have lead to emerge different swarm intelligence-based optimization techniques. Within these swarm intelligence-based optimization techniques, Artificial Bee Colony (ABC) algorithm has a special position due to its less control parameters, robust, phase-divided and easily implementable structures. Although standard workflow of ABC algorithm is capable of producing optimal or near optimal solutions for numerous problems, there are still some intelligent operations that are not directly modeled for the ABC algorithm in order to maintain the reduced complexity of the implementation and small number of control parameters. In this study, ABC algorithm is tried to be powered with a more realistic dancing approach called time-based information sharing, for short tb, model. The proposed model is integrated into the workflow of the standard ABC algorithm and its well-known variants. Experimental studies carried out on both classical and bound constrained single-objective CEC2015 benchmark functions showed that the proposed model in which the dancing durations of the employed bees are determined by the fitness values of the memorized sources significantly improved the performance of the standard and other variants of the ABC algorithm.