L
Laith Abualigah
Researcher at Amman Arab University
Publications - 301
Citations - 12424
Laith Abualigah is an academic researcher from Amman Arab University. The author has contributed to research in topics: Computer science & Metaheuristic. The author has an hindex of 30, co-authored 111 publications receiving 4223 citations. Previous affiliations of Laith Abualigah include Universiti Sains Malaysia & Al al-Bayt University.
Papers
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Journal ArticleDOI
The Arithmetic Optimization Algorithm
Laith Abualigah,Ali Diabat,Ali Diabat,Seyedali Mirjalili,Mohamed Abd Elaziz,Mohamed Abd Elaziz,Amir H. Gandomi +6 more
TL;DR: Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms.
Journal ArticleDOI
Aquila Optimizer: A novel meta-heuristic optimization algorithm
Laith Abualigah,Dalia Yousri,Mohamed Abd Elaziz,Ahmed A. Ewees,Mohammed A. A. Al-qaness,Amir H. Gandomi +5 more
TL;DR: From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed.
Journal ArticleDOI
Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer
S Jain,Laith Abualigah,Laith Abualigah,Mohamed Abd Elaziz,Putra Sumari,Zong Woo Geem,Amir H. Gandomi +6 more
TL;DR: In this paper, the authors proposed a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles.
Journal ArticleDOI
A comprehensive review
Asaju La’aro Bolaji,Mohammed Azmi Al-Betar,Mohammed A. Awadallah,Ahamad Tajudin Khader,Laith Abualigah +4 more
TL;DR: The comprehensive review of Krill Herd Algorithm as applied to different domain is presented, which covers the applications, modifications, and hybridizations of the KH algorithms.
Book
Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering
TL;DR: A new feature selection method using particle swarm optimization algorithm with a novel weighting scheme and a detailed dimension reduction technique are proposed to obtain a new subset of more informative features with low-dimensional space to improve the performance of the text clustering (TC) algorithm.