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 hybr...
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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 optimizationalgorithms. In this paper, two different hybrid models are designed based on spottedhyenaoptimization (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 optimizationalgorithms. The experimental results con firm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimizationalgorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.
One of the major challenges in cyber space and Internet of things (IoT) environments is the existence of fake or phishing websites that steal users' information. A website as a multimedia system provides access to...
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One of the major challenges in cyber space and Internet of things (IoT) environments is the existence of fake or phishing websites that steal users' information. A website as a multimedia system provides access to different types of data such as text, image, video, audio. Each type of these data are prune to be used by fishers to perform a phishing attack. In phishing attacks, people are directed to fake pages and their important information is stolen by a thief or phisher. Machine learning and data mining algorithms are the widely used algorithms for classifying websites and detecting phishing attacks. Classification accuracy is highly dependent on the feature selection method employed to choose appropriate features for classification. In this research, an improved spotted hyena optimization algorithm (ISHO algorithm) is proposed to select proper features for classifying phishing websites through support vector machine. The proposed ISHO algorithm outperformed the standard spotted hyena optimization algorithm with better accuracy. In addition, the results indicate the superiority of ISHO algorithm to three other meta-heuristic algorithms including particle swarm optimization, firefly algorithm, and bat algorithm. The proposed algorithm is also compared with a number of classification algorithms proposed before on the same dataset.
In recent years, metaheuristic methods have been preferred for the optimum design of automobile components, and important results have been accomplished. In this paper, a comparison of the whale optimizationalgorithm...
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In recent years, metaheuristic methods have been preferred for the optimum design of automobile components, and important results have been accomplished. In this paper, a comparison of the whale optimizationalgorithm (WOA), the ant lion algorithm(ALO), and the spotted hyena optimization algorithm (SHOA) are presented to show how these optimization methods have been exploited to achieve weight reduction in an automobile brake pedal while maintaining stress requirements. This research is the first in the literature elucidating the application of the SHOA for the optimum design of automobile components. optimization using the SHOA results in a reduction of 18.1 % of brake pedal weight.
In this research, a novel optimizationalgorithm, which is a hybrid spottedhyena-Nelder-Mead optimizationalgorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known gri...
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In this research, a novel optimizationalgorithm, which is a hybrid spottedhyena-Nelder-Mead optimizationalgorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known grinding optimization problem is solved to prove the superiority of the HSHO-NM over other algorithms. The results of the HSHO-NM are compared with others. The results show that HSHO-NM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding operations.
The application of swarm optimizationalgorithm in WSNs has become a new research hotspot of scholars at home and abroad. Aiming at the problem that the spotted hyena optimization algorithm is easy to fall into local ...
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ISBN:
(纸本)9781450396899
The application of swarm optimizationalgorithm in WSNs has become a new research hotspot of scholars at home and abroad. Aiming at the problem that the spotted hyena optimization algorithm is easy to fall into local optimum, which leads to low optimization accuracy, an improved spotted hyena optimization algorithm is proposed. On the basis of the original algorithm, Sine chaotic map and elite reverse learning strategy are embedded to reduce the probability of falling into local optimum and improve the global search ability of spotted hyena optimization algorithm. In addition, the adaptive inertia weight is introduced to balance the global search and local development capabilities of the spotted hyena optimization algorithm. The experimental results show that compared with the original spotted hyena optimization algorithm, sine and cosine algorithm, multiverse optimizationalgorithm, differential evolution algorithm and particle swarm optimizationalgorithm, the improved algorithm has significant performance advantages in optimization ability and stability.
To improve the accuracy of offshore wind power foundation corrosion rate prediction and grasp the operation status of equipment in time, an offshore wind power foundation corrosion rate prediction model based on an im...
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To improve the accuracy of offshore wind power foundation corrosion rate prediction and grasp the operation status of equipment in time, an offshore wind power foundation corrosion rate prediction model based on an improved spottedhyenaoptimization (SHO) algorithm is proposed in this paper. Firstly, in order to reduce the modeling workload of the offshore wind power foundation corrosion prediction model, kernel principal component analysis (KPCA) is used to extract the principal elements of the offshore wind power foundation corrosion rate. Secondly, for the problems in the SHO algorithm, it is easy to fall into local optimums, and the solution accuracy is not high;the SHO algorithm is improved by the convergence factor and Levy flight strategy, which gives the SHO algorithm stronger global search ability and convergence speed. Finally, based on the improved SHO algorithm, an offshore wind power base corrosion rate prediction model is established by optimizing the penalty parameter and kernel function parameter. Simulation results show that the average relative error, root mean square error, and global maximum relative error assimilation coefficient of the combined prediction model in this paper are 2.86%, 0.15, 3.74%, and 0.995, respectively, which are better than other corrosion prediction models.
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