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Nadakinamani, R. G., A. Reyana, S. Kautish, A. S. Vibith, Y. Gupta, S. F. Abdelwahab, and A. W. Mohamed, "[Retracted] Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques", Computational intelligence and neuroscience, vol. 2022, issue 1: Hindawi, pp. 2973324, 2022. Abstract
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Nadakinamani, R. G., A. Reyana, S. Kautish, A. S. Vibith, Y. Gupta, S. F. Abdelwahab, and A. W. Mohamed, "[Retracted] Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques", Computational intelligence and neuroscience, vol. 2022, issue 1: Hindawi, pp. 2973324, 2022. Abstract
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Goudos, S. K., A. Boursianis, A. W. Mohamed, M. Salucci, S. Koulouridis, and C. Christodoulou, "Wideband antenna design for 5g mmwave applications using enhanced adaptive differential evolution", 2022 IEEE international symposium on antennas and propagation and USNC-URSI radio science meeting (AP-S/URSI): IEEE, pp. 63-64, 2022. Abstract
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El-Lamey, M. M., M. M. Eid, M. Gamal, N. - E. M. Bishady, and A. W. Mohamed, "Using machine learning algorithms for breast cancer diagnosis", International Journal of Applied Metaheuristic Computing (IJAMC), vol. 12, issue 4: IGI Global, pp. 117-137, 2021. Abstract
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Tawfik, R. M., H. A. A. Nomer, M. Saeed Darweesh, A. W. Mohamed, and H. Mostafa, "UAV-Assisted IoT Data Collection Optimization Using Gaining-Sharing Knowledge Algorithm", Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume II: Solving Constrained Single Objective Real-Parameter Optimization Problems: Springer International Publishing Cham, pp. 135-146, 2022. Abstract
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Tawfik, R. M., H. A. A. Nomer, S. M. Darweesh, A. W. Mohame, and H. Mostafa, "UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm.", Computers, Materials & Continua, vol. 72, issue 3, 2022. Abstract
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Hassan, S. A., P. Agrawal, T. Ganesh, and A. W. Mohamed, "A Travelling Disinfection-Man Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing Knowledge-Based Optimization Algorithm", Intelligent Data Analysis for COVID-19 Pandemic, Singapore, Springer Singapore, pp. 291 - 318, 2021. Abstract

An improved scheduling the disinfection process of the new coronavirus (COVID-19) is introduced. The scheduling aims at achieving the best utilization of the available day time, which is calculated as the total disinfection time minus the total loss travelling time. In this regard, a new application problem is presented, which is called a travelling disinfection-man problem (TDP). The new problem (TDP) in network optimization resemble somehow the famous travelling salesman problems (TSP) but with basic distinct variations where a disinfection group is likely to select a route to reach a subset of predetermined places to be disinfected with the most utilization of the available day working hours. A nonlinear binary model is introduced with a detailed real application case study involving the improving the scheduling of coronavirus disinfection process for five contaminated faculties in Ain Shams University in Cairo, and the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK).

Hassan, S. A., P. Agrawal, T. Ganesh, and A. W. Mohamed, "A travelling disinfection-man problem (TDP) for covid-19: a nonlinear binary constrained gaining-sharing knowledge-based optimization algorithm", Intelligent data analysis for COVID-19 pandemic: Springer Singapore Singapore, pp. 291-318, 2021. Abstract
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Hassan, S. A., P. Agrawal, T. Ganesh, and A. W. Mohamed, "Traveling Advisor Problem in Occupational Health and Safety Field with a Case Study from Egypt", Africa Case Studies in Operations Research: A Closer Look into Applications and Algorithms: Springer International Publishing Cham, pp. 1-24, 2022. Abstract
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Mohamed, M., K. M. Sallam, and A. W. Mohamed, "Transition supply chain 4.0 to supply chain 5.0: innovations of industry 5.0 technologies toward smart supply chain partners", Neutrosophic systems with applications, vol. 10, pp. 1-11, 2023. Abstract
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Li, C., G. Xiong, X. Fu, A. W. Mohamed, X. Yuan, M. A. Al-Betar, and P. N. Suganthan, "Takagi–Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm", Knowledge-Based Systems, vol. 255: Elsevier, pp. 109773, 2022. Abstract
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Kong, L. S., M. B. Jasser, S. - S. M. Ajibade, and A. W. Mohamed, "A systematic review on software reliability prediction via swarm intelligence algorithms", Journal of King Saud University-Computer and Information Sciences: Elsevier, pp. 102132, 2024. Abstract
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Chen, E., J. Chen, A. W. Mohamed, B. Wang, Z. Wang, and Y. Chen, "Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization", IEEE Access, vol. 8, pp. 175660 - 175668, 2020. Abstract
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Chen, E., J. Chen, A. W. Mohamed, B. Wang, Z. Wang, and Y. Chen, "Swarm intelligence application to UAV aided IoT data acquisition deployment optimization", IEEE Access, vol. 8: IEEE, pp. 175660-175668, 2020. Abstract
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, "STRATEGIC DECISION SUPPORT SYSTEM BASED HYBRID MODELS FOR COLLEGES ENROLLMENT CAPACITY PLANNING:DESIGN & IMPLEMENTATION", The Online Journal of Science and Technology, vol. 7, issue 2, pp. 100-110, 2017. Abstract
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Hassan, S. A., Y. M. Ayman, K. Alnowibet, P. Agrawal, and A. W. Mohamed, "Stochastic Travelling Advisor Problem Simulation with a Case Study: A Novel Binary Gaining-Sharing Knowledge-Based Optimization Algorithm", Complexity, vol. 2020: Hindawi, pp. 6692978, 2020. AbstractWebsite

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK (BGSK) depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained.

Hassan, S. A., K. Alnowibet, M. H. Khodeir, P. Agrawal, A. F. Alrasheedi, and A. W. Mohamed, "A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling", Computers, Materials & Continua, vol. 69, issue 1: Tech Science Press, pp. 849-871, 2021. Abstract
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Mohamed, A. W., Solving stochastic programming problems using new approach to Differential Evolution algorithm, , vol. 18, issue 2, pp. 75 - 86, 2017. AbstractWebsite

This paper presents a new approach to Differential Evolution algorithm for solving stochastic programming problems, named DESP. The proposed algorithm introduces a new triangular mutation rule based on the convex combination vector of the triangle and the difference vector between the best and the worst individuals among the three randomly selected vectors. The proposed novel approach to mutation operator is shown to enhance the global and local search capabilities and to increase the convergence speed of the new algorithm compared with conventional DE. DESP uses Deb’s constraint handling technique based on feasibility and the sum of constraint violations without any additional parameters. Besides, a new dynamic tolerance technique to handle equality constraints is also adopted. Two models of stochastic programming (SP) problems are considered: Linear Stochastic Fractional Programming Problems and Multi-objective Stochastic Linear Programming Problems. The comparison results between the DESP and basic DE, basic particle swarm optimization (PSO), Genetic Algorithm (GA) and the available results from where it is indicated that the proposed DESP algorithm is competitive with, and in some cases superior to, other algorithms in terms of final solution quality, efficiency and robustness of the considered problems in comparison with the quoted results in the literature.

Mohamed, A. W., "Solving stochastic programming problems using new approach to Differential Evolution algorithm", Egyptian Informatics Journal, vol. 18, issue 2: ELSEVIER, pp. 75-86, 2017. Abstract
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MohameD, A., D. Oliva, and P. N. Suganthan, Solving Single Objective Bound-constrained Real-parameter Numerical Optimization Problems, : Springer, 2022. Abstract
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Mohamed, A. W., Solving large-scale global optimization problems using enhanced adaptive differential evolution algorithm, , vol. 3, issue 4, pp. 205 - 231, 2017. AbstractWebsite

This paper presents enhanced adaptive differential evolution (EADE) algorithm for solving high-dimensional optimization problems over continuous space. To utilize the information of good and bad vectors in the DE population, the proposed algorithm introduces a new mutation rule. It uses two random chosen vectors of the top and bottom 100p% individuals in the current population of size NP, while the third vector is selected randomly from the middle [NP-2(100p%)] individuals. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the only one of the two mutation rules is applied with the probability of 0.5. This new mutation scheme helps to maintain effectively the balance between the global exploration and local exploitation abilities for searching process of the DE. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which, in turn, can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 7 and 20 standard high-dimensional benchmark numerical optimization problems for both the IEEE CEC-2008 and the IEEE CEC-2010 Special Session and Competition on Large-Scale Global Optimization. The comparison results between EADE and its version and the other state-of-art algorithms that were all tested on these test suites indicate that the proposed algorithm and its version are highly competitive algorithms for solving large-scale global optimization problems.

Mohamed, A. W., "Solving large-scale global optimization problems using enhanced adaptive differential evolution algorithm", Complex & Intelligent Systems, vol. 3, issue 4: Springer, pp. 205-231, 2017. Abstract
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Mohamed, A. W., "Solving large-scale global optimization problems using enhanced adaptive differential evolution algorithm", Complex & Intelligent Systems, vol. 3, issue 4: Springer, pp. 205-231, 2017. Abstract
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