# A Bee Colony-Based Optimized Searching Mechanism in the Internet of Things
## 2 Literature Review
### Enhanced Variants of ABC Schemes  
This sub-section explores the variants of the ABC algorithm where researchers have modified the existing functionality to achieve better results as per its applicability. [PERSON] et al. presented an enhanced version of the ABC algorithm named the quick artificial bee colony algorithm (qABC). In qABC, the authors pointed out that onlooker bees choose their food sources differently as compared to employed bees. Thus, a new definition for the onlooker bee is introduced in qABC, while, in the original ABC technique, both the employed and onlooker bees select their food sources using the same formula. Another modification introduced in this algorithm is that it chooses a solution in neighboring areas using the mean Euclidian distance. Moreover, the efficiency of qABC was tested on various benchmark functions considering the neighborhood radius. The experimental outcomes prove the efficiency of the qABC algorithm [21]. [PERSON] et al. presented an improved quick artificial bee colony algorithm (iqABC). A new definition for exploitation is presented in this work for the purpose of enhancing the early convergence rate without affecting the final solutions. To attain encouraging results, four different search schemas are added in the ABC algorithm. Additionally, a finestLimit parameter is added in the workflow of the standard ABC algorithm. Experiments were performed for the finestLimit parameter and various benchmark problems were used to test the performance of the iqABC algorithm while comparing it with some other well-known algorithms. The results show the efficiency of the iqABC algorithm over other preceding algorithms [22]. [PERSON] et al. present the IABC algorithm, which enhances the modified search strategy of the GABC with a dynamic inertia weight factor. Experimental results demonstrate that the IABC outperforms both the standard ABC and GABC in terms of search accuracy and convergence speed [23]. [PERSON] et al. presented an improved algorithm named EMABC-NS that utilizes multi-strategy collaboration and a neighborhood search. It incorporates information from both the global best individual and nearby individuals during the search, enhancing the search strategy for employed and onlooker bees. The introduction of a modification rate (MR) randomizes the solution dimensions. The EMABC-NS outperforms competitors in benchmark functions and engineering problems, demonstrating its effectiveness in practical applications [24]. [PERSON] et al. presented an ABC algorithm named FOABC that uses fractional-order calculus to enhance the local search capabilities by incorporating memory properties. The FOABC  
Figure 1: Intelligent behavior of bees in ABC.  
refers to past foraging behaviors stored in memory when generating new candidate solutions. An improved search strategy in the employed bee phase balances diversification and intensification. Experimental results on CEC benchmark problems show the FOABC's superiority over other ABC variants and its effectiveness in practical applications like robot path planning [25]. [PERSON] et al. presented an enhanced MaOABC-TA algorithm, incorporating two archives (convergence and diversity) and three search strategies, inspired by the Two_Arch2 method. A new probability selection strategy prioritizes diverse solutions. Experimental comparisons with 10 MaOEAs and 3 AfBCs on benchmark sets reveal that MaOABC-TA outperforms the others in terms of the inverted generational distance (IGD) and hypervolume (HV) values [26].