Results 21 to 30 of about 10,782 (162)
IEEE Access, 2022 Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling.
Osamah Mohammed Alyasiri +3 more
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Permutation Tests for Metaheuristic Algorithms
Mathematics, 2022 Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the ...
Mahamed G. H. Omran +4 more
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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms
IEEE Access, 2020 Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the ...
Jorge Daniel Mello-Roman +1 more
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Energies, 2020 In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring.
Gianfranco Chicco, Andrea Mazza
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Buildings, 2022 This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm’s accuracy and local convergence in finite element (FE) model updating.
Shiqiang Qin +3 more
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A novel metaheuristic optimization algorithm: the monarchy metaheuristic
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2019 In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems.
Ibtissam Ahmia, Méziane Aïder
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A Multiple-Search Multi-Start Framework for Metaheuristics for Clustering Problems
IEEE Access, 2020 Metaheuristic algorithms have been widely used as an effective and efficient way for solving various complex optimization problems; there is, however, plenty of room for improvement.
Kai-Cheng Hu +2 more
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MetaCluster: An open-source Python library for metaheuristic-based clustering problems
SoftwareX, 2023 Clustering, based on metaheuristic algorithms, is a rapidly developing field. Its goal is to use these methods to reframe clustering issues as optimization problems. In this study, we propose an open-source library named MetaCluster.
Nguyen Van Thieu +2 more
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Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 2023 Many optimization problems are complex, challenging and take a significant amount of computational effort to solve. These problems have gained the attention of researchers and they have developed lots of metaheuristic algorithms to use for solving these ...
Aydın Sipahioğlu, İslam Altın
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Open Mathematics, 2017 Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a ...
Bansal Shonak +2 more
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