Feature selection (FS) represents an important task in classification. Hadith represents an example in which we can apply FS on it. Hadiths are the second major source of Islam after the Quran. ...Thousands of Hadiths are available in Islam, and these Hadiths are grouped into a number of classes. In the literature, there are many studies conducted for Hadiths classification. Sine Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. SCA algorithm is mainly based on exploring the search space using sine and cosine mathematical formulas to find the optimal solution. However, SCA, like other Optimization Algorithm (OA), suffers from the problem of local optima and solution diversity. In this paper, to overcome SCA problems and use it for the FS problem, two major improvements were introduced to the standard SCA algorithm. The first improvement includes the use of singer chaotic map within SCA to improve solutions diversity. The second improvement includes the use of the Simulated Annealing (SA) algorithm as a local search operator within SCA to improve its exploitation. In addition, the Gini Index (GI) is used to filter the resulted selected features to reduce the number of features to be explored by SCA. Furthermore, three new Hadith datasets were created. To evaluate the proposed Improved SCA (ISCA), the new three Hadiths datasets were used in our experiments. Furthermore, to confirm the generality of ISCA, we also applied it on 14 benchmark datasets from the UCI repository. The ISCA results were compared with the original SCA and the state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA), and the most recent optimization algorithm, Harris Hawks Optimizer (HHO). The obtained results confirm the clear outperformance of ISCA in comparison with other optimization algorithms and Hadith classification baseline works. From the obtained results, it is inferred that ISCA can simultaneously improve the classification accuracy while it selects the most informative features.
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.
Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types ...of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called
β
-hill climbing (
β
HC) and denoted by ABC–
β
HC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC–
β
HC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon’s rank sum statistical test was conducted, and the
p
values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC–
β
HC is statistically significant.
Fault diagnosis is crucial for the successful deployment and operation of mobile ad-hoc networks (MANETs). Comparison-based fault diagnosis is a practical approach to identify the status of nodes ...based on tasks they execute. This approach has been applied for MANETs by Chessa and Santi. Their model exploits the nature of shared communication in ad-hoc networks to enhance the efficiency of the diagnosis process. In this article, we present a survey on comparison-based system-level fault diagnosis protocols applied for MANETs. After fault classification, we focus on classifying the protocols considering their dissemination mechanisms into three categories namely: flooding-based, spanning-tree-based, and clustering-based protocols. We provide a qualitative comparison among various protocols in terms of algorithm execution behaviors, complexity, experimental outcomes, and scalability. In addition, we reveal the capacity of each protocol under practical MANET scenarios. We then conclude the survey discussing several research issues open for further investigation.
High dimensionality of data represents a major problem that affects the accuracy of the classification. This problem related with classification is mainly resulted from the availability of irrelevant ...features. Feature selection represents a solution to a problem by selecting the most informative features and discard the irrelevant features. Generalized normal distribution optimization (GNDO) represents a newly developed optimization that confirmed its outperformance in comparison with well-known optimization algorithms on parameter extraction for photovoltaic models. As an optimization algorithm, however, GNDO suffers from degraded performance when dealing with a problem with a high dimensionality. The main problems of GNDO include exploitation problem by falling into local optima problem. Also, GNDO has solutions diversity problem when it deals with data with high dimensionality. To alleviate the drawbacks of this algorithm and solve feature selection problems, a local search algorithm (LSA) is used. The new algorithm is called dynamic generalized normal distribution optimization (DGNDO), which includes the following main improvements to GNDO: it can improve the best solution to solve the local optima problem, it can improve solution diversity by improving the randomly selected solution, and it can improve both exploration and exploitation combined. To confirm the outperformance and efficiency of the new DGNDO algorithm, DGNDO algorithm is applied on 20 benchmarked datasets from UCI repository of data. In addition, DGNDO algorithm results are compared with seven well-known optimization algorithms using number of evaluation metrics including classification, accuracy, fitness, the number of selected features, statistical results using Wilcoxon test and convergence curves. The obtained results reveal the superiority of DGNDO algorithm over all other competing algorithms.
Fault diagnosis has always been vital to providing a high level of dependability in systems. The comparison approach is one of the most prevalent diagnosis techniques that offers a simple and yet ...practical way to identify faulty nodes in a system. Even though several comparison-based diagnostic models have already been introduced, the majority of them only diagnose permanent faults in static networks. Nowadays, intermittent faults and dynamic systems are more challenging to diagnose and become more common. This paper, first, proposes a novel comparison-based diagnostic model that deals with hybrid fault model in mobile networks. Both the diagnosable systems and faults under the proposed model have been characterized. Second, this paper proposes an efficient fault diagnosis protocol for hybrid faults in mobile networks. The proposed protocol employs a network coding technique to exchange the diagnosis messages so that it can provide a correct diagnosis with a higher probability of completeness. The correctness and complexity proofs of the proposed protocol are presented, and they show the viability of the proposed diagnostic model and protocol for hybrid faults in mobile networks. Besides, we study and analyse the performance of the proposed protocol under various fault and system parameters using OMNeT++ simulation. The simulation results show that our protocol can diagnose hybrid faults in mobile networks with high accuracy and less overhead.
This paper describes a new comparison-based model for fault diagnosis in wireless ad hoc networks. Fault diagnosis is crucial for ensuring the dependability of systems. Wireless ad hoc networks are ...highly prone to faults as consequence of their dynamical conditions. The comparison approach is a practical diagnosis model that has been used to develop self-diagnosis systems in wired and wireless networks. This approach can detect and diagnose hard and soft faults in systems. The traditional fault diagnostic models were designed for static networks. Thus, they cannot provide complete and correct fault diagnosis in mobile wireless networks. In this paper, we introduce a time-free self-diagnosis model that respects the design requirements of mobile wireless networks. That is, it adapts to the topology’s changes, it imposes no known bounds on time delays, and it requires limited network information. Further, we develop a fault diagnosis protocol that can correctly diagnose faulty nodes undergoing static and dynamic faults in mobile ad-hoc networks (MANETs). Both an analytical model and a simulation study have been used to prove and evaluate the efficiency of our protocol under various scenarios. Furthermore, the performance of our protocol is compared with related protocols. The results show that our proposed protocol is efficient in terms of communication and time complexity.
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