Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

Abstract

The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

“…In the SHO, it is assumed that the elite search agent knows about the prey location and other individuals try to update their positions by making a cluster-reliable group of friends-towards the elite agent. Further information about the ruling equations of the SHO can be found in previous studies [44][45][46].…”

“…This work was done for tackling the drawbacks of the neural network, such as the fall into local optima. In research by Jia et al [45], the SHO was employed to select features. However, the optimal solutions it found were enhanced by an embedded simulated annealing algorithm.…”

Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

“…To establish the competence of SHO algorithm, Panda et al used the same method to train neural network to resolve real‐life difficulties in terms of data classification. In 2019, Jia et al hybridized SHO algorithm with another popular metaheuristic algorithm termed as simulated annealing, to unravel feature selection problem by considering 20 standard datasets. Recently, Soto et al contributed the notion about set covering problem by the help of SHO and the same is utilized for solving diversified practical problems.…”

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

“…These hybrid algorithms [17,18,19,20] tend to perform better by improving their exploration and exploitation ability (through inclusion of local or global search techniques). Some researchers are currently using a combination of filter and wrapper to reach a better solution by using advantages of both the models.…”