# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 2 Related Work  
SI algorithms are well-known for their global exploration capability and are gaining more attention by the feature selection community recently. It has been proven by the well-known \"No Free Lunch (NFL) theorem\" [42] that there is no heuristic algorithm that can solve all types of optimization problems. Specifically, since the exploration--exploitation balance is an unsolved issue within SI algorithms, each SI algorithm introduces an experimental solution through the combination of deterministic models and stochastic principles. Under such conditions, each SI algorithm holds distinctive characteristics that properly satisfy the requirements of particular problems [18]. Therefore, a particular SI algorithm is not able to solve all problems adequately. This motivates many researchers to investigate the effectiveness of different algorithms in different fields. Between 2010 and 2020, there have been a total of 85 papers used SI algorithms for feature selection in different fields [30]. For the medical application, [PERSON] et al. integrated the node centrality and PSO algorithm [43] to improve the performance on FS. [PERSON] et al. [44] applied the sine-cosine algorithm and the disruption operator to Salp Swarm Algorithm (SSA) to improve the accuracy of disease diagnosis. [PERSON] and [PERSON] [45] proposed a novel Whale Optimization Algorithm (WOA) for FS, and the crossover and mutation operators are used to enhance the exploitation of the WOA algorithm. An FS method suppressed less relevant features in the breast cancer datasets by ABC. Then, to minimize the potential of ABC being trapped in a local optimum, the accuracy of classification by GBDT is employed to evaluate the quality of the inputs [46]. To select a DNA microarray subset of relevant and non-redundant features for computational complexity reduction, [PERSON] et al. [47] proposed a two-phase hybrid model based on improved-binary PSO (iBPSO). A recursive PSO method was developed by [PERSON] et al. [48] for gene selection. Ahead of this, an Ant Colony Optimization-selection (ACO-S) is utilized to generate a gene subset with the smallest size and salient features while yielding high classification accuracy [49]. Furthermore, [PERSON] et al. [50] hybridized the V-WSP, proposed by [PERSON] et al. [51], with PSO to improve the accuracy of laser-induced breakdown spectroscopy. Moreover, to solve the feature selection problem for acoustic defect detection, a single-objective feature selection algorithm hybridizing the Shuffled Frog Leaping Algorithm (SFLA) with an improved minimum-redundancy maximum-relevancy (ImRMR) was proposed by [PERSON] et al. [52]. To handle the challenges of the network detection that detecting anomalies from high dimensional network traffic feature is time-consuming, an FA-based feature selection was attempted by [PERSON] and [PERSON] [53]. to obtain an optimized detection rate. In addition, FS methods based on the firefly algorithm were investigated for Arabic text classification. Ref. [54] and facial expression classification [55]. Additionally, various SI algorithms have been applied to FS in QSAR. [PERSON] et al. [56] first used multi-layer variable selection strategy, and then used GA to select meaningful descriptors from a large set of initial descriptors. PSO has been widely applied to selection descriptors in QSAR. For instance, [PERSON] et al. [57] modified PSO named PSO-PLS for variable selection in MLR and PLS modeling. The hybridization of PSO with GA are used as a FS technique by [PERSON] et al. [58]. After that, [PERSON] et al. [59] proposed a weighted sampling PSO-PLS (WS-PSO-PLS) to select the optimal descriptor subset in the QSAR/QSPR model. Moreover, the improved binary Pigeon Optimization Algorithm(POA) was applied to selecting the most relevant descriptors (variables) in QSAR/QSPR classification models [60]. Compared with PSO and ACO, there are fewer studies applying ABC algorithm to FS.