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Institution

Amman Arab University

EducationAmman, Jordan
About: Amman Arab University is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Optimization problem & Metaheuristic. The organization has 167 authors who have published 303 publications receiving 3316 citations.


Papers
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Journal ArticleDOI
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.

1,218 citations

Journal ArticleDOI
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.

989 citations

Journal ArticleDOI
TL;DR: In this paper, the authors empirically test the relationship between intellectual capital (i.e., human capital, structural capital, relational capital) and business performance within the pharmaceutical sector of Jordan.
Abstract: Purpose – The purpose of this study is to empirically test the relationship between intellectual capital (i.e. human capital, structural capital, relational capital) and business performance within the pharmaceutical sector of Jordan.Design/methodology/approach – A valid research instrument was utilized to conduct a survey of 132 top‐ and middle‐level managers from all 15 members of the Jordanian Association of Pharmaceutical Manufacturers.Findings – A correlation and path analysis were conducted to ascertain the validity of the measures and models. Statistical support was found for the hypothesized relationships.Research limitations/implications – The findings offer valuable insights on the generalizability of intellectual capital in a novel research setting.Practical implications – Intellectual capital measurement is of primary interest for senior executives of pharmaceutical firms in Jordan.Originality/value – The research reported is among only a few to investigate the issue of intellectual capital in...

508 citations

Journal ArticleDOI
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.
Abstract: This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods.

457 citations

Journal ArticleDOI
TL;DR: This work presents a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments and reveals that MALO outperformed other well-known optimization algorithms.
Abstract: Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results.

223 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
20229
202194
202058
201931
201812