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All Journal International Journal of Electrical and Computer Engineering
Jaradat, Ghaith M.
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Computer Science & IT Electrical & Electronics Engineering

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A novel population-based local search for nurse rostering problem Abuhamdah, Anmar; Boulila, Wadii; Jaradat, Ghaith M.; Quteishat, Anas M.; Alsmadi, Mutasem K.; Almarashdeh, Ibrahim A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (14.644 KB) | DOI: 10.11591/ijece.v11i1.pp471-480

Abstract

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.
Enhancing routing efficiency in highway environments of vehicular ad hoc networks through fuzzy logic-based protocols Al Shugran, Mahmoud A.; Abu-Al-Aish, Ahmad; Jaradat, Ghaith M.; Alghamdi, Fahad Ali; Alqurni, Jehad Saad; Alsmadi, Mutasem Khalil; AL Hawamdeh, Majd; Alfagham, Hayat; Badawi, Usama A.; Gharaibeh, Mutaz Falah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp493-504

Abstract

The predictive directional greedy routing (PDGR) protocol is widely utilized in highway settings within vehicular ad hoc networks (VANETs). However, PDGR encounters a notable challenge when packets lack a suitable vehicle directionally, leading to network disconnections. This triggers a shift to carry and forward recovery mode due to outdated neighbor information in the vehicle's neighbor table (VNT). To address this, our study proposes an improved fuzzy logic-based improved PDGR (IPDGR). This novel algorithm dynamically adjusts beaconing intervals based on real-time network dynamics. Through comprehensive evaluation using VANET simulators, IPDGR demonstrates superior performance compared to PDGR and directional greedy routing (DGR) protocols across various metrics including Inconsistency of vehicle's neighbor's table (IVNT), packet delivery ratio (PDR), routing path length (RPL), and number of hole problem occurrence (NHPO).
A hybrid adaptive neuro-fuzzy inference system and reptile search algorithm model for wind power forecasting Al-Widyan, Mohamad I.; Abualigah, Laith; Jaradat, Ghaith M.; Alsmadi, Mutasem Khalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2857-2873

Abstract

Estimating the number of wind ranches generated in the upcoming minutes, hours, or days is the focus of wind power forecasting. Deep learning has garnered a lot of interest in wind control estimation because of how well they perform classification, grouping, and recurrence. The adaptive neuro-fuzzy inference system was successfully applied in wind power forecasting. However, its performance relies on optimal selection of hyperparameters. This study introduces a novel predictive model by incorporating the reptile search algorithm with adaptive neuro-fuzzy inference system (ANFIS) for short-term wind power forecasting. It employs reptile search algorithm (RSA), known for adjustable parameters, disentangled search, and consistent outcomes, to optimize ANFIS’s hyperparameters. Additionally, via exploitation during training, RSA performs a selection of best features in the dataset that contributes to the classification accuracy of ANFIS. This aims to enhance precision of the anticipated yield. Employing authentic wind power data from Jordan is undertaken to evaluate efficiency. The performance is compared with alternative techniques, including artificial neural networks, random forests, and support vector machines. Findings showed that ANFIS-RSA performs competitively for the well-known Chinese benchmark dataset (99.9% accuracy; 0.99 R2; 10.54 MAE; 11.62 RMSE) and is more robustly accurate than others over the Jordanian dataset (0.84.6% accuracy; 0.96 R2; 0.098 MAE; 0.203 RMSE).
Co-Authors Abu-Al-Aish, Ahmad Abualigah, Laith Abuhamdah, Anmar AL Hawamdeh, Majd Al Shugran, Mahmoud A. Al-Widyan, Mohamad I. Alfagham, Hayat Alghamdi, Fahad Ali Almarashdeh, Ibrahim A. Alqurni, Jehad Saad Alsmadi, Mutasem K. Alsmadi, Mutasem Khalil Badawi, Usama A. Boulila, Wadii Gharaibeh, Mutaz Falah Quteishat, Anas M.