H
Hossam Faris
Researcher at University of Jordan
Publications - 185
Citations - 15014
Hossam Faris is an academic researcher from University of Jordan. The author has contributed to research in topics: Metaheuristic & Feature selection. The author has an hindex of 40, co-authored 178 publications receiving 7977 citations. Previous affiliations of Hossam Faris include University of Salento.
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Salp Swarm Algorithm
Seyedali Mirjalili,Amir H. Gandomi,Seyedeh Zahra Mirjalili,Shahrzad Saremi,Hossam Faris,Seyed Mohammad Mirjalili +5 more
TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
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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.