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Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes.
Mohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Yunyoung Nam; Attia El-Fergany. Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules. Electronics 2021, 10, 1846 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Mohamed Abouhawwash, Yunyoung Nam, Attia El-Fergany. Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules. Electronics. 2021; 10 (15):1846.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Yunyoung Nam; Attia El-Fergany. 2021. "Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules." Electronics 10, no. 15: 1846.
In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.
Mohamed Abdel-Basset; Reda Mohamed; Safaa Saber; S. Askar; Mohamed Abouhawwash. Modified Flower Pollination Algorithm for Global Optimization. Mathematics 2021, 9, 1661 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Safaa Saber, S. Askar, Mohamed Abouhawwash. Modified Flower Pollination Algorithm for Global Optimization. Mathematics. 2021; 9 (14):1661.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Safaa Saber; S. Askar; Mohamed Abouhawwash. 2021. "Modified Flower Pollination Algorithm for Global Optimization." Mathematics 9, no. 14: 1661.
The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.
Mohamed Abdel-Basset; Victor Chang; Hossam Hawash; Ripon K. Chakrabortty; Michael Ryan. Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey. Annals of Operations Research 2021, 1 -49.
AMA StyleMohamed Abdel-Basset, Victor Chang, Hossam Hawash, Ripon K. Chakrabortty, Michael Ryan. Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey. Annals of Operations Research. 2021; ():1-49.
Chicago/Turabian StyleMohamed Abdel-Basset; Victor Chang; Hossam Hawash; Ripon K. Chakrabortty; Michael Ryan. 2021. "Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey." Annals of Operations Research , no. : 1-49.
Efficient and accurate estimations of unidentified parameters of photovoltaic (PV) models are essential to their simulation. This study suggests two new variants of the whale optimization algorithm (WOA) for identifying the nine parameters of the three-diode PV model. The first variant abbreviated as RWOA is based on integrating the WOA with ranking methods under a novel updating scheme to utilize each whale within the population as much as possible during the optimization process. The second variant, namely HWOA, has been based on employing a novel cyclic exploration-exploitation operator with the RWOA to promote its local and global search for averting stagnation into local minima and accelerating the convergence speed in the right direction of the near-optimal solution. Experimentally, RWOA and HWOA are validated on a solar cell (RTC France) and two PV modules (Photowatt-PWP201 and Kyocera KC200GT). Further, these proposed variants are compared with five well-known parameter extraction models in order to demonstrate their notable advantages over the other existing competing algorithms for minimizing the root mean squared error (RMSE) between experimentally measured data and estimated one. The experimental findings show that RWOA is superior in some observed cases and superior in the other cases in terms of final accuracy and convergence speed; yet, HWOA is superior in all cases.
Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Sameh Askar; Mohamed Abouhawwash. Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations. Energies 2021, 14, 3729 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Sameh Askar, Mohamed Abouhawwash. Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations. Energies. 2021; 14 (13):3729.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Sameh Askar; Mohamed Abouhawwash. 2021. "Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations." Energies 14, no. 13: 3729.
These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications.
Abdullah Lakhan; Muhammad Suleman Memon; Qurat-Ul-Ain Mastoi; Mohamed Elhoseny; Mazin Abed Mohammed; Mumtaz Qabulio; Mohamed Abdel-Basset. Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing 2021, 1 -23.
AMA StyleAbdullah Lakhan, Muhammad Suleman Memon, Qurat-Ul-Ain Mastoi, Mohamed Elhoseny, Mazin Abed Mohammed, Mumtaz Qabulio, Mohamed Abdel-Basset. Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing. 2021; ():1-23.
Chicago/Turabian StyleAbdullah Lakhan; Muhammad Suleman Memon; Qurat-Ul-Ain Mastoi; Mohamed Elhoseny; Mazin Abed Mohammed; Mumtaz Qabulio; Mohamed Abdel-Basset. 2021. "Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network." Cluster Computing , no. : 1-23.
Optimum modeling of the proton exchange membrane fuel cell (PEMFC) has attracted considerable research over the last decades to simulate, control, evaluate, manage, and optimize the performance of PEMFC stacks. The main problem in optimal modeling is that the model parameters are not provided by manufacturers, and the empirical dataset points are not sufficient to accurately model the cell. Therefore, a new approach based on the improved chimp optimization algorithm (IChOA) is proposed to define the uncertain parameters of the PEMFC. A ranking-based updating strategy and a balanced exploration and exploitation strategy (BEES) are employed here within the IChOA. In the first strategy, the unbeneficial solutions in the population are replaced with other solutions covering other regions, which are unreachable by the original one. The second strategy aims at utilizing iteration as much as possible so that, at the beginning, the method maximizes the exploration operator in the first half of the optimization process to ensure the balance between the exploration and exploitation framework; and then, in the second half, the exploitation capability is maximized attempting to find a better solution than the best-so-far. The proposed IChOA is validated by three well-known commercial PEMFCs, namely 250 W stack, Ballard Mark V, and AVISTA SR-12 500 W modular. The best results of the IChOA are compared with 15 nature-inspired metaheuristics algorithms and another one known as gradient-based optimizer under various statistical analyses and under varied operating conditions. The superiority of the IChOA is demonstrated in terms of convergence stability, and final accuracy.
Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Ripon K. Chakrabortty; Michael J. Ryan. Adaptive and Efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis. Energy 2021, 233, 121096 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Ripon K. Chakrabortty, Michael J. Ryan. Adaptive and Efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis. Energy. 2021; 233 ():121096.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "Adaptive and Efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis." Energy 233, no. : 121096.
The development of sustainable green buildings (GBs) is a major contribution to the preservation of the environment. Sustainable thinking in GB construction is not a supplementary element, but rather necessary to achieve the building’s functional, economic, and environmental efficiency in order to preserve resources and meet current and future needs. In particular, developing countries can apply the idea of sustainability in GBs by following international policies and standards, combined with their local characteristics, to construct GBs that are aligned with the environment and are in line with the available local capabilities and resources. The paper focuses on the dimensions and indicators of sustainable design for GBs in developing countries to achieve the positive dimensions of building sustainability, such as preserving energy and natural resources, water management, adaptation to the surrounding environment, and respecting the needs of its users. We assess and prioritize the dimensions and indicators of GBs through the use of a multi-criteria decision-making (MCDM) method under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators. The relative importance of the selected dimensions and indicators is assessed through the analytical hierarchy method (AHP) method. The results indicate that the water efficiency dimension is the most significant, with a weight of 0.330, while the energy efficiency dimension is the least significant for GBs in developing countries, with a weight of 0.100. The paper concludes with a set of administrative implications for applying sustainable development strategies in GBs.
Mohamed Abdel-Basset; Abduallah Gamal; Ripon Chakrabortty; Michael Ryan; Nissreen El-Saber. A Comprehensive Framework for Evaluating Sustainable Green Building Indicators under an Uncertain Environment. Sustainability 2021, 13, 6243 .
AMA StyleMohamed Abdel-Basset, Abduallah Gamal, Ripon Chakrabortty, Michael Ryan, Nissreen El-Saber. A Comprehensive Framework for Evaluating Sustainable Green Building Indicators under an Uncertain Environment. Sustainability. 2021; 13 (11):6243.
Chicago/Turabian StyleMohamed Abdel-Basset; Abduallah Gamal; Ripon Chakrabortty; Michael Ryan; Nissreen El-Saber. 2021. "A Comprehensive Framework for Evaluating Sustainable Green Building Indicators under an Uncertain Environment." Sustainability 13, no. 11: 6243.
Modern information technology, such as the internet of things (IoT) provides a real-time experience into how a system is performing and has been used in diversified areas spanning from machines, supply chain, and logistics to smart cities. IoT captures the changes in surrounding environments based on collections of distributed sensors and then sends the data to a fog computing (FC) layer for analysis and subsequent response. The speed of decision in such a process relies on there being minimal delay, which requires efficient distribution of tasks among the fog nodes. Since the utility of FC relies on the efficiency of this task scheduling task, improvements are always being sought in the speed of response. Here, we suggest an improved elitism genetic algorithm (IEGA) for overcoming the task scheduling problem for FC to enhance the quality of services to users of IoT devices. The improvements offered by IEGA stem from two main phases: first, the mutation rate and crossover rate are manipulated to help the algorithms in exploring most of the combinations that may form the near-optimal permutation; and a second phase mutates a number of solutions based on a certain probability to avoid becoming trapped in local minima and to find a better solution. IEGA is compared with five recent robust optimization algorithms in addition to EGA in terms of makespan, flow time, fitness function, carbon dioxide emission rate, and energy consumption. IEGA is shown to be superior to all other algorithms in all respects.
Mohamed Abdel‐Basset; Reda Mohamed; Ripon K. Chakrabortty; Michael J. Ryan. IEGA: An improved elitism‐based genetic algorithm for task scheduling problem in fog computing. International Journal of Intelligent Systems 2021, 1 .
AMA StyleMohamed Abdel‐Basset, Reda Mohamed, Ripon K. Chakrabortty, Michael J. Ryan. IEGA: An improved elitism‐based genetic algorithm for task scheduling problem in fog computing. International Journal of Intelligent Systems. 2021; ():1.
Chicago/Turabian StyleMohamed Abdel‐Basset; Reda Mohamed; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "IEGA: An improved elitism‐based genetic algorithm for task scheduling problem in fog computing." International Journal of Intelligent Systems , no. : 1.
This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.
Mohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Victor Chang; S. Askar. A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling. Applied Sciences 2021, 11, 4837 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Mohamed Abouhawwash, Victor Chang, S. Askar. A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling. Applied Sciences. 2021; 11 (11):4837.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Victor Chang; S. Askar. 2021. "A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling." Applied Sciences 11, no. 11: 4837.
One of the key challenges in cyber-physical systems (CPS) is the dynamic fitting of data sources under multivariate or mixture distribution models to determine abnormalities. Equations of the models have been statistically characterized as nonlinear and non-Gaussian ones, where data have high variations between normal and suspicious data distributions. To address nonlinear equations of these distributions, a cuckoo search algorithm is employed. In this paper, the cuckoo search algorithm is effectively improved with a novel strategy, known as a convergence speed strategy, to accelerate the convergence speed in the direction of the optimal solution for achieving better outcomes in a small number of iterations when solving systems of nonlinear equations. The proposed algorithm is named an improved cuckoo search algorithm (ICSA), which accelerates the convergence speed by improving the fitness values of function evaluations compared to the existing algorithms. To assess the efficacy of ICSA, 34 common nonlinear equations that fit the nature of cybersecurity models are adopted to show if ICSA can reach better outcomes with high convergence speed or not. ICSA has been compared with several well-known, well-established optimization algorithms, such as the slime mould optimizer, salp swarm, cuckoo search, marine predators, bat, and flower pollination algorithms. Experimental outcomes have revealed that ICSA is superior to the other in terms of the convergence speed and final accuracy, and this makes a promising alternative to the existing algorithm.
Mohamed Abdel-Basset; Reda Mohamed; Nazeeruddin Mohammad; Karam Sallam; Nour Moustafa. An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems. Mathematics 2021, 9, 1140 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Nazeeruddin Mohammad, Karam Sallam, Nour Moustafa. An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems. Mathematics. 2021; 9 (10):1140.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Nazeeruddin Mohammad; Karam Sallam; Nour Moustafa. 2021. "An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems." Mathematics 9, no. 10: 1140.
To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.
Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Mohamed Abouhawwash; S. Askar. Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm. Mathematics 2021, 9, 995 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Mohamed Abouhawwash, S. Askar. Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm. Mathematics. 2021; 9 (9):995.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Mohamed Abouhawwash; S. Askar. 2021. "Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm." Mathematics 9, no. 9: 995.
Reverse logistics (RL) is considered the reverse manner of gathering and redeploying goods at the end of their lifetime span from consumers to manufacturers in order to reutilize, dispose, or remanufacture. Whereas RL has many economic benefits, it presents compromises to businesses that wish to remain competitive but be responsible global citizens in terms of social, environmental, risk, and safety aspects of sustainable development. Managing RL systems therefore is considered a multifaceted mission that necessities a significant level of technology, infrastructure, experience, and competence. Consequently, various commerce institutions are looking to outsourcing their RL actions to third-party reverse logistics providers (3PRLPs). In this work, a novel hybrid multiple-criteria decision-making (MCDM) framework is proposed to classify and choose 3PRLPs, which comprises the analytic hierarchy process (AHP) technique, and technique for order of preference by similarity to ideal solution (TOPSIS) technique under neutrosophic environment. Accordingly, AHP is availed for defining weights of key dimensions and their subindices. In addition, TOPSIS was adopted for ranking the specified 3PRLPs. The efficiency of the proposed approach is clarified through application on a considered car parts manufacturing industry case in Egypt, which shows the features of the combined MCDM methods. A comparative and sensitivity analyses were performed to highlight the benefits of the incorporated MCDM methods and for clarifying the effect of changing weights in selecting the sustainable 3PRLP alternative, respectively. The suggested framework is also shown to present more functional execution when dealing with uncertainties and qualitative inputs, demonstrating applicability to a broad range of applications. Ultimately, the best sustainable 3PRLPs were selected and results show that social, environmental, and risk and safety sustainability factors have the greatest influence when determining 3PRLPs alternatives.
Mohamed Abdel-Basset; Abduallah Gamal; Mohamed Elhoseny; Ripon Chakrabortty; Michael Ryan. A Conceptual Hybrid Approach from a Multicriteria Perspective for Sustainable Third-Party Reverse Logistics Provider Identification. Sustainability 2021, 13, 4615 .
AMA StyleMohamed Abdel-Basset, Abduallah Gamal, Mohamed Elhoseny, Ripon Chakrabortty, Michael Ryan. A Conceptual Hybrid Approach from a Multicriteria Perspective for Sustainable Third-Party Reverse Logistics Provider Identification. Sustainability. 2021; 13 (9):4615.
Chicago/Turabian StyleMohamed Abdel-Basset; Abduallah Gamal; Mohamed Elhoseny; Ripon Chakrabortty; Michael Ryan. 2021. "A Conceptual Hybrid Approach from a Multicriteria Perspective for Sustainable Third-Party Reverse Logistics Provider Identification." Sustainability 13, no. 9: 4615.
Although photovoltaic (PV) energy production offers several environmental and commercial advantages, the irregular nature of PV energy can challenge the design and development of the energy management systems. Precise forecasting for PV energy production is therefore of vital importance to supply consumers to improve trust in functionality of the energy management system. Stimulated by current developments in deep learning (DL) techniques as well as the promising efficiency in energy-related applications, this study introduces a novel DL architecture, called PV-Net, for short-term forecasting of day-ahead PV energy. In PV-Net, the gates of the gated recurrent unit (GRU) are redesigned using convolutional layers (called Conv-GRU) to enable efficient extraction of positional and temporal characteristics in the PV power sequences. The Conv-GRU cells are stacked in bidirectional (Bi-dir) blocks to enable modeling temporal information in forward and backward directions. The Bi-dir block is residually connected to avoid information loss across layers and to facilitate gradient flow during training. A real-world case study from Alice Springs, Australia, is employed to evaluate and compare the performance of the proposed PV-Net against recent cutting-edge approaches. The values of four performance measures demonstrate the efficiency of the proposed PV-Net in terms of prediction precision and consistency.
Mohamed Abdel-Basset; Hossam Hawash; Ripon K. Chakrabortty; Michael Ryan. PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production. Journal of Cleaner Production 2021, 303, 127037 .
AMA StyleMohamed Abdel-Basset, Hossam Hawash, Ripon K. Chakrabortty, Michael Ryan. PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production. Journal of Cleaner Production. 2021; 303 ():127037.
Chicago/Turabian StyleMohamed Abdel-Basset; Hossam Hawash; Ripon K. Chakrabortty; Michael Ryan. 2021. "PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production." Journal of Cleaner Production 303, no. : 127037.
The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.
Mohamed Abdel-Basset; Reda Mohamed; Ripon Chakrabortty; Michael Ryan; Attia El-Fergany. An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models. Energies 2021, 14, 1867 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Ripon Chakrabortty, Michael Ryan, Attia El-Fergany. An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models. Energies. 2021; 14 (7):1867.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Ripon Chakrabortty; Michael Ryan; Attia El-Fergany. 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models." Energies 14, no. 7: 1867.
With the significant growth of multiprocessor systems (MPS) to deal with complex tasks and speed up their execution, the energy generated as a result of this growth becomes one of the significant limits to that growth. Although several traditional techniques are available to deal with this challenge, they don’t deal with this problem as multi-objective to optimize both energy and makespan metrics at the same time, in addition to expensive cost and memory usage. Therefore, this paper proposes a multi-objective approach to tackle the task scheduling for MPS based on the modified sine-cosine algorithm (MSCA) to optimize the makespan and energy using the Pareto dominance strategy; this version is abbreviated as energy-aware multi-objective MSCA (EA-M2SCA). The classical SCA is modified based on dividing the optimization process into three phases. The first phase explores the search space as much as possible at the start of the optimization process, the second phase searches around a solution selected randomly from the population to avoid becoming trapped into local minima within the optimization process, and the last searches around the best-so-far solution to accelerate the convergence. To further improve the performance of EA-M2SCA, it was hybridized with the polynomial mutation mechanism in two effective manners to accelerate the convergence toward the best-so-far solution with preserving the diversity of the solutions; this hybrid version is abbreviated as EA-MHSCA. Finally, the proposed algorithms were compared with a number of well-established multi-objective algorithms: EA-MHSCA is shown to be superior in most test cases.
Mohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Ripon K. Chakrabortty; Michael J. Ryan. EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis. Expert Systems with Applications 2021, 173, 114699 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Mohamed Abouhawwash, Ripon K. Chakrabortty, Michael J. Ryan. EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis. Expert Systems with Applications. 2021; 173 ():114699.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis." Expert Systems with Applications 173, no. : 114699.
Proton Exchange Membrane fuel cells (PEMFCs) are a promising renewable energy source to convert the chemical reactions between hydrogen and oxygen into electricity. To simulate, evaluate, manage, and optimize PEMFCs, an accurate mathematical model is essential. Therefore, this paper improves the accuracy of a mathematical model for the PEMFC based on semi-empirical equations by proposing a meta-heuristic technique to optimize its unidentified parameters. Because the I–V characteristic curve of the PEMFC systems has a nonlinear and multivariable nature, conventional optimization techniques are difficult and time-consuming but modern meta-heuristic algorithms are ideally suited. Therefore, in this paper, a new improved optimization algorithm based on the Heap-based optimizer (HBO) has been proposed to estimate the unknown parameters of PEMFCs models using an objective function that minimizes the error between the measured and estimated data. This improved HBO (IHBO) effectively uses two strategies: ranking-based position update (RPU) and Lévy-based exploitation improvement (LEI) to improve the final accuracy to the SSE value with higher convergence speed. Four well-known commercial PEMFCs, (the 500 W BCS stack, NetStack PS6, H-12 stack, and AVISTA SR-12 500 W modular) are utilized to verify the proposed IHBO and compare it with 11 popular optimizers using various performance metrics. The experimental findings show the superiority of IHBO in terms of convergence speed, stability, and final accuracy, where IHBO could fulfill fitness values of 0.01170, 2.14570, 0.11802, and 0.00014 for the 500 W BCS stack, NetStack PS6, H-12 stack, and AVISTA SR-12 500 W modular, respectively.
Mohamed Abdel-Basset; Reda Mohamed; Mohamed Elhoseny; Ripon K. Chakrabortty; Michael J. Ryan. An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies. International Journal of Hydrogen Energy 2021, 46, 11908 -11925.
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Mohamed Elhoseny, Ripon K. Chakrabortty, Michael J. Ryan. An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies. International Journal of Hydrogen Energy. 2021; 46 (21):11908-11925.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Mohamed Elhoseny; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies." International Journal of Hydrogen Energy 46, no. 21: 11908-11925.
In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIEGA and IEGA are validated on three common benchmarks and compared with a number of well-known robust evolutionary and meta-heuristic algorithms to check their efficacy. The experimental results show that HIEGA and IEGA are competitive with others for the datasets incorporated in the comparison, such as Carlier, Reeves, and Heller.
Mohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Ripon Chakrabortty; Michael Ryan. A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem. Mathematics 2021, 9, 270 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Mohamed Abouhawwash, Ripon Chakrabortty, Michael Ryan. A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem. Mathematics. 2021; 9 (3):270.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Ripon Chakrabortty; Michael Ryan. 2021. "A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem." Mathematics 9, no. 3: 270.
Feature selection refers to a process used to reduce the dimension of a dataset in order to obtain the optimal features subset for machine learning and data mining algorithms. This aids the achievement of higher classification accuracy in addition to reducing the training time of a learning algorithm as a result of the removal of redundant and less-informative features. In this paper, four binary versions of the slime mould algorithm (SMA) are proposed for feature selection, in which the standard SMA is incorporated with the most appropriate transfer function of eight V-Shaped and S-Shaped transfer functions. The first version converts the standard SMA, which has not been used yet for feature selection to the best of our knowledge, into a binary version (BSMA). The second, abbreviated as TMBSMA, integrates BSMA with two-phase mutation (TM) to further exploit better solutions around the best-so-far. The third version, abbreviated as AFBSMA, combines BSMA with a novel attacking-feeding strategy (AF) that trades off exploration and exploitation based on the memory saving of each particle. Finally, TM and AF are integrated with BSMA to produce better solutions, in a version called FMBSMA. The k-nearest neighbors (KNN) algorithm, one of the common classification and regression algorithms in machine learning, is used to measure the classification accuracy of the selected features. To validate the performance of the four proposed versions of BSMA, 28 well-known datasets are employed from the UCI repository. The experiments confirm the efficacy of the AF method in providing better results. Furthermore, after comparing the four versions, the FMBSMA version is shown to be the best compared with the other three versions and six state-of-art feature selection algorithms.
Mohamed Abdel-Basset; Reda Mohamed; Ripon K. Chakrabortty; Michael J. Ryan; SeyedAli Mirjalili. An efficient binary slime mould algorithm integrated with a novel attacking-feeding strategy for feature selection. Computers & Industrial Engineering 2021, 153, 107078 .
AMA StyleMohamed Abdel-Basset, Reda Mohamed, Ripon K. Chakrabortty, Michael J. Ryan, SeyedAli Mirjalili. An efficient binary slime mould algorithm integrated with a novel attacking-feeding strategy for feature selection. Computers & Industrial Engineering. 2021; 153 ():107078.
Chicago/Turabian StyleMohamed Abdel-Basset; Reda Mohamed; Ripon K. Chakrabortty; Michael J. Ryan; SeyedAli Mirjalili. 2021. "An efficient binary slime mould algorithm integrated with a novel attacking-feeding strategy for feature selection." Computers & Industrial Engineering 153, no. : 107078.
Many countries that derive their energy from fossil fuel sources are turning to renewable, and environmentally friendly energy sources to alleviate the growing concerns about global warming and environmental issues. Bioenergy technology is one of the potential alternatives for renewable energy systems. This paper evaluates sustainable bioenergy production technologies through a case study in Egypt. A comprehensive methodology has been developed in which experts and decision makers are able to use linguistic terms to express their opinions and participate in the decision making required to prioritize the dimensions that affect the sustainability of bioenergy production technologies. Identification of the optimum bioenergy production technology and setting its priorities is a difficult task; many dimensions must be taken into account in the evaluation process, such as the environmental, technical, economic, and social dimensions and their sub-indicators. This paper therefore applies a hybrid multi-criteria decision-making (MCDM) approach that takes into account many conflicting dimensions. In addition, handling of uncertainty was conducted under a neutrosophic environment using trapezoidal neutrosophic numbers (TNNs). Initially, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to identify the relative importance of the dimensions and their sub-indicators. Then, Evaluation based on Distance from Average Solution (EDAS) was employed to rank alternatives. An illustrative case considering seven bioenergy production technologies was studied to confirm the feasibility of the suggested approach, and comparisons were performed to show the advantages of the hybrid MCDM techniques. Sensitivity analysis is conducted to prove the validity and stability of the developed framework with variations in weights. The results of this work provide useful information for energy policy decision makers and the results of the case study indicate that the conversion of agricultural and municipal wastes to biogas is the most suitable sustainable bioenergy technology with a weight of 0.996 followed by oil crops to biodiesel technology with weight 0.539.
Mohamed Abdel-Basset; Abduallah Gamal; Ripon K. Chakrabortty; Michael Ryan. Development of a hybrid multi-criteria decision-making approach for sustainability evaluation of bioenergy production technologies: A case study. Journal of Cleaner Production 2021, 290, 125805 .
AMA StyleMohamed Abdel-Basset, Abduallah Gamal, Ripon K. Chakrabortty, Michael Ryan. Development of a hybrid multi-criteria decision-making approach for sustainability evaluation of bioenergy production technologies: A case study. Journal of Cleaner Production. 2021; 290 ():125805.
Chicago/Turabian StyleMohamed Abdel-Basset; Abduallah Gamal; Ripon K. Chakrabortty; Michael Ryan. 2021. "Development of a hybrid multi-criteria decision-making approach for sustainability evaluation of bioenergy production technologies: A case study." Journal of Cleaner Production 290, no. : 125805.
The demand for energy in Egypt has increased dramatically due to the steady increase in economic and societal development. To meet this need, the use of more renewable energy resources is an essential part of the solution to the ultimate shortage of energy. Because of the multitude of factors involved, the selection of the most suitable renewable energy systems (RESs) is a multi-criteria decision-making (MCDM) problem. There are a good number of works associated with the design of MCDM methods, particularly under uncertain and ambiguous situations. However, efficient incorporation of ambiguity and uncertainty in decision making is still a challenging task, and thus this work proposes a hybrid MCDM approach for selecting the components of a sustainable RES under uncertain environments, utilizing different triangular neutrosophic numbers to deal with unclear information. This proposed hybrid approach starts with defining the relative importance of the selected sustainable criteria by using an Analytic Hierarchy Process (AHP). Later, in the second phase, this hybrid approach combines VIKOR and the TOPSIS to rank the RESs for a real-life case study. The results obtained indicate that concentrated solar power is the most suitable source of renewable energy for Egypt, with photoelectric power the second most suitable.
Mohamed Abdel-Basset; Abduallah Gamal; Ripon K. Chakrabortty; Michael J. Ryan. Evaluation approach for sustainable renewable energy systems under uncertain environment: A case study. Renewable Energy 2021, 168, 1073 -1095.
AMA StyleMohamed Abdel-Basset, Abduallah Gamal, Ripon K. Chakrabortty, Michael J. Ryan. Evaluation approach for sustainable renewable energy systems under uncertain environment: A case study. Renewable Energy. 2021; 168 ():1073-1095.
Chicago/Turabian StyleMohamed Abdel-Basset; Abduallah Gamal; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "Evaluation approach for sustainable renewable energy systems under uncertain environment: A case study." Renewable Energy 168, no. : 1073-1095.