Artificial Bee Colony (ABC) algorithm that mimics the intelligent foraging behaviors of real honey bees has been successfully applied to different types of optimization problems in recent years. Actually, the main reason lying behind the high preference of the ABC algorithm is related with its good performance on solving difficult optimization problems due to the effective search mechanisms existing in a single cycle and easily-implementable bee phases. However, with the purpose of increasing the implementation simplicity and generalizing the principal concept of the algorithm, some significant behaviors of the real foraging bees are not closely simulated and integrated into the workflow of the ABC or tried to be managed by employing simple randomized or conditional operations. In this study, in order to increase the performance of ABC algorithm, the complex behavior of the foraging employed bees related with how they decide to pass through to the dance area and how long they stay on there for informing onlookers is modeled in detail and then a new variant of ABC called intelligent forager forwarding ABC for short iff - ABC is proposed. For analyzing the possible contributions of the intelligent forager forwarding strategy on the performance of the ABC algorithm, thirteen classical benchmark problems and fifteen computationally expensive benchmark problems presented at the CEC 2015 were tested. The results obtained from the experimental studies were compared with the results of the different meta-heuristics in addition to the well-known variants of the standard ABC algorithm. From the experimental studies, it was concluded that the intelligent forager forwarding strategy significantly improves the quality of the final solutions and the convergence speed of the ABC algorithm. (C) 2020 Elsevier B.V. All rights reserved.