*3.4. ABCs*

ABC was designed with several key members: a nectar source, nectar, and three types of bees [89]. The nectar amount from the flower represents the function value, and the food location means the solution. The nectar source and employed and onlooker bees are in quantity the same and the nectar source corresponds to the employed bees. Onlooker bees rely on nectar and employed bees to find flowers, and scout bees randomly fly to seek flowers near the hive [90].

In [91], the authors combined TLBO and ABC to design a method (TLABC) that included three search phases. The employed bee stage combined a teaching mechanism, the onlooker bee stage combined a learning mechanism, and the reconnaissance bee combined a generalized reversal mechanism. In [92], Wu et al. designed a new ABC (ABCTRR) by combining ABCs' exploiting capability with the trust-region reflective technique's exploiting capability. In [93], a new algorithm (IABC) was designed to solve ABC's early convergence issue by dividing the employed bee into two parts, one unchanged and the other searching the domain of the optimal global position. The identified parameters illustrated the high accuracy of IABC. For the integration of exploitation and exploration well, Tefek [94] combined ABC with a local search method to develop a new approach (ABC-Ls). Comparison revealed that ABC-Ls were more accurate, faster, and more stable. In [95], the authors compared ABC with PSO, showing that ABC outperformed PSO in all aspects of the results. In [96], a fitness distance balance mechanism was applied to TLABC to reconstruct a new method (FDB-TLABC). Experimental results confirmed the excellent performance of FDB-TLABC.

In Table 6, ABC-TRR has the least TNFES, followed by ABC, TLABC, IABC, ABC-Ls, and FDB-TLABC. There is an order-of-magnitude difference in resource consumption between ABC-TRR and the other variants of ABC. Table 7 compiles the experimental results. FDB-TLABC ranks first in combined MIN RMSE, followed by ABC-Ls, ABC-TRR, and TLABC. Therefore, achieving another increase in accuracy with less resource consumption for ABC is a priority for future research.
