No abstract

No abstract

No abstract

Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test (P-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

No abstract

The rapid increase in data volume and features dimensionality have a negative influence on machine learning and many other fields, such as decreasing classification accuracy and increasing computational cost. Feature selection technique has a critical role as a preprocessing step in reducing these issues. It works by eliminating the features that may negatively influence the classifiers' performance, such as irrelevant, redundant and less informative features. This paper aims to introduce an improved Harris hawks optimization (IHHO) by utilizing elite oppositebased learning and proposing a new search mechanism. Harris hawks optimization (HHO) is a novel metaheuristic general-purpose algorithm recently introduced to solve continuous search problems. Compared to conventional HHO, the proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities. Moreover, it improves population diversity, computational accuracy, and accelerates convergence rate. To evaluate the performance of IHHO, we conducted a series of experiments on twenty benchmark datasets collected from the UCI repository and the scikit-feature project. The datasets represent different levels of feature dimensionality, such as low, moderate, and high. Further, four criteria were adopted to determine the superiority of IHHO: classification accuracy, fitness value, number of selected features, and statistical tests. Furthermore, a comparison between IHHO and other well-known algorithms such as Generic algorithm (GA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Slime Mould Algorithm (SMA) was performed. The experimental results have confirmed the dominance of IHHO over the other optimization algorithms in different aspects, such as accuracy, fitness value, and feature selection.

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.