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Ibrahim Aljarah
Researcher at University of Jordan
Publications - 135
Citations - 11266
Ibrahim Aljarah is an academic researcher from University of Jordan. The author has contributed to research in topics: Feature selection & Metaheuristic. The author has an hindex of 39, co-authored 125 publications receiving 6054 citations. Previous affiliations of Ibrahim Aljarah include North Dakota State University & Jacksonville University.
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Harris hawks optimization: Algorithm and applications
Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah,Majdi Mafarja,Huiling Chen +6 more
TL;DR: The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques.
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Optimizing connection weights in neural networks using the whale optimization algorithm
TL;DR: The qualitative and quantitative results prove that the proposed WOA-based trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.
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Grey wolf optimizer: a review of recent variants and applications
TL;DR: In this review paper, several research publications using GWO have been overviewed and summarized and the main foundation of GWO is provided, which suggests several possible future directions that can be further investigated.
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Grasshopper optimization algorithm for multi-objective optimization problems
TL;DR: A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone and then a mechanism is proposed to use the model in approximating the global optimum in a single-objective search space.
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An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
Hossam Faris,Majdi Mafarja,Ali Asghar Heidari,Ibrahim Aljarah,Ala' M. Al-Zoubi,Seyedali Mirjalili,Hamido Fujita +6 more
TL;DR: Two new wrapper FS approaches that use SSA as the search strategy are proposed and it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.