Abstract
Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.
Highlights
The optimization process is existing in several research areas such as engineering, medical, agriculture, computer science, and feature selection
Compared to the binary versions of the-state-of-the-art optimization techniques of the original Grey Wolf Optimizer (GWO) [1], Stochastic Fractal Search (SFS) [23], Particle Swarm Optimization (PSO) [24], hybrid of PSO and GWO [21], Satin Bowerbird Optimizer (SBO) [25], Whale Optimization Algorithm (WOA) [26], Multiverse Optimization (MVO) [27], and Firefly Algorithm (FA) [28], in addition to, Genetic Algorithm (GA) [29] and hybrid of GA and GWO, the results show the superiority of the proposed algorithm
DIRECTIONS This paper proposed a modified binary GWO algorithm based on a stochastic fractal search technique (MbGWO-SFS) that is used with the K-Nearest Neighbor (KNN) classifier to select the optimal subset of features for different problems by achieving the exploration and exploitation balance
Summary
The optimization process is existing in several research areas such as engineering, medical, agriculture, computer science, and feature selection. GWO has the advantages of simplicity, flexibility, deprivation-free mechanism, and the ability to avoid the local optima Because of that, it has been used in many research areas in the last years such as feature subset selection [1], DC motors control [14], [15], solving optimal reactive power dispatch problem [16], financial crisis prediction [13], and in some applications, the GWO algorithm was used to train the Multilayer Perceptron (MLP) network [17]. To solve the feature selection problems, a binary GWO algorithm is integrated with a multi-phase mutation in [7] based on the wrapper methods.
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