Dr. Ali Wagdy Mohamed El-Naggar
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Dr. Ali Wagdy Mohamed El-Naggar

Associate Professor
Department of Operations Research, Faculty of graduate Studies for Statistical Research, Cairo University

Highest Degree
Ph.D. in Operations Research from Cairo University, Egypt

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Biography

Dr. Ali Wagdy Mohamed El-Naggar is currently working as Assistant Professor at Department of Statistics and Operations Research-Faculty of Science-King Abdul-Aziz University-Jeddah-Kingdom of Saudi Arabia. He obtained his PhD in Operations Research from Cairo University, Egypt in 2010. His area of research interest related to Mathematical Programming (Deterministic and Stochastic Optimization) Simulation Optimization and Decision Support Systems, Artificial Intelligence for Global Optimization, Statistical Models, and Project Management and Project Economics. He received research grants from King Abdulaziz University. He also received Distinguished Scientific Research & Publication Quality Award from King Abdulaziz University, and Cairo University. He has published 6 articles in refereed journals, and 4 papers in refereed conferences. He is also working as Associate Editor in Aloy Journal of Soft Computing and Applications, and Editorial Board Member in Computer and Information Science (Canadian Center of Science and Education). He is also serving as reviewer of different journals and conferences. He supervised 1 MSc thesis.

Area of Interest:

Computer Sciences
100%
Mathematical Programming
62%
Decision Support Systems
90%
Artificial Intelligence
75%
Statistical Models
55%

Research Publications in Numbers

Books
0
Chapters
16
Articles
79
Abstracts
15

Selected Publications

  1. Xu, S., G. Xiong, A.W. Mohamed and H.R.E.H. Bouchekara, 2022. Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options. Energy, Vol. 256. 10.1016/j.energy.2022.124511.
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  2. Sharma, A.K., A.M. Alshamrani, K.A. Alnowibet, A.F. Alrasheedi, A. Saxena and A.W. Mohamed, 2022. A demand side management control strategy using RUNge kutta optimizer (RUN). Axioms, Vol. 11. 10.3390/axioms11100538.
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  3. Saxena, A., A.F. Alrasheedi, K.A. Alnowibet, A.M. Alshamrani, S. Shekhawat and A.W. Mohamed, 2022. Local grey predictor based on cubic polynomial realization for market clearing price prediction. Axioms, Vol. 11. 10.3390/axioms11110627.
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  4. Pradhan, A., S.K. Bisoy, S. Kautish, M.B. Jasser and A.W. Mohamed, 2022. Intelligent decision-making of load balancing using deep reinforcement learning and parallel PSO in cloud environment. IEEE Access, 10: 76939-76952.
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  5. Mohapatra, P.K., S.K. Rout, S.K. Bisoy, S. Kautish, M. Hamzah, M.B. Jasser and A.W. Mohamed, 2022. Application of bat algorithm and its modified form trained with ANN in channel equalization. Symmetry, Vol. 14. 10.3390/sym14102078.
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  6. Li, C., G. Xiong, X. Fu, A.W. Mohamed, X. Yuan, M.A. Al-Betar and P.N. Suganthan, 2022. Takagi-sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm. Knowl. Based Syst., Vol. 255. 10.1016/j.knosys.2022.109773.
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  7. Emambocus, B.A.S., M.B. Jasser, A. Amphawan and A.W. Mohamed, 2022. An optimized discrete dragonfly algorithm tackling the low exploitation problem for solving TSP. Mathematics, Vol. 10. 10.3390/math10193647.
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  8. Chen, Y., D. Pi, S. Yang, Y. Xu, J. Chen and A.W. Mohamed, 2022. HNIO: A hybrid nature-inspired optimization algorithm for energy minimization in UAV-assisted mobile edge computing. IEEE Trans. Netw. Serv. Manage., 19: 3264-3275.
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  9. Alnowibet, K.A., S. Mahdi, A.M. Alshamrani, K.M. Sallam and A.W. Mohamed, 2022. A family of hybrid stochastic conjugate gradient algorithms for local and global minimization problems. Mathematics, Vol. 10. 10.3390/math10193595.
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  10. Abdel-Basset, M., H. Hawash, K.A. Alnowibet, A.W. Mohamed and K.M. Sallam, 2022. Interpretable deep learning for discriminating pneumonia from lung ultrasounds. Mathematics, Vol. 10. 10.3390/math10214153.
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  11. Wu, W., H. Ouyang, A.W. Mohamed, C. Zhang and S. Li, 2019. Enhanced harmony search algorithm with circular region perturbation for global optimization problems. Applied Intell., (In Press). 10.1007/s10489-019-01558-6.
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  12. Mohamed, A.W., A.K. Mohamed, E.Z. Elfeky and M. Saleh, 2019. Enhanced directed differential evolution algorithm for solving constrained engineering optimization problems. Int. J. Applied Metaheuristic Comput., 10: 1-28.
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  13. Mohamed, A.W., A.A. Hadi and K.M. Jambi, 2019. Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm Evol. Comput., Vol. 50. 10.1016/j.swevo.2018.10.006.
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  14. Mohamed, A.W. and A.K. Mohamed, 2019. Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int. J. Mach. Learn. Cybern., 10: 253-277.
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  15. Mohamed, A.K., A.W. Mohamed, E.Z. Elfeky and M. Saleh, 2019. Solving Constrained Non-Linear Integer and Mixed-Integer Global Optimization Problems using Enhanced Directed Differential Evolution Algorithm. In: Machine Learning Paradigms: Theory and Application, Hassanien, A.E. (Ed.). Springer, Cham, Switzerland, ISBN: 978-3-030-02357-7, pp: 327-349.
  16. Mohamed, A.K. and A.W. Mohamed, 2019. Real-Parameter Unconstrained Optimization Based on Enhanced AGDE Algorithm. In: Machine Learning Paradigms: Theory and Application, Hassanien, A.E. (Ed.). Springer, Cham, Switzerland, ISBN: 978-3-030-02357-7, pp: 431-450.
  17. Hadi, A.A., A.W. Mohamed and K.M. Jambi, 2019. LSHADE-SPA memetic framework for solving large-scale optimization problems. Complex Intell. Syst., 5: 25-40.
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  18. Mohamed, A.W., 2018. Differential Evolution (DE): A short review. Robot. Autom. Eng. J., Vol. 2, No. 1. 10.19080/RAEJ.2018.02.555579.
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  19. Mohamed, A.W., 2018. A novel differential evolution algorithm for solving constrained engineering optimization problems. J. Intell. Manuf., 29: 659-692.
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  20. Mohamed, A.W. and P.N. Suganthan, 2018. Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput., 22: 3215-3235.
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  21. Mohamed, A.K. and A.W. Mohamed, 2018. Control parameters in Differential Evolution (DE): A short review. Robot. Autom. Eng. J., Vol. 3, No. 2. 10.19080/RAEJ.2018.03.555607.
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  22. Mohamed, A.W., 2017. Solving stochastic programming problems using new approach to differential evolution algorithm. Egypt. Inform. J., 18: 75-86.
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  23. Mohamed, A.W., 2017. Solving large-scale global optimization problems using enhanced adaptive differential evolution algorithm. Complex Intell. Syst., 3: 205-231.
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  24. Mohamed, A.W., 2017. An efficient modified differential evolution algorithm for solving constrained non-linear integer and mixed-integer global optimization problems. Int. J. Mach. Learn. Cybern., 8: 989-1007.
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  25. Mohamed, A.W. and A.S. Almazyad, 2017. Differential evolution with novel mutation and adaptive crossover strategies for solving large scale global optimization problems. Applied Comput. Intell. Soft Comput., Vol. 2017. 10.1155/2017/7974218.
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  26. Mohamed, A.W., 2016. A New Modified Binary Differential Evolution Algorithm and its Applications. Appl. Math. Inf. Sci., 10: 1965-1969.
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  27. Mohamed, A.W. and S.A. El-Quliti, 2016. A Large-Scale Nonlinear Mixed-Binary Goal Programming Model to Assess Candidate Locations for Solar Energy Stations: An Improved Binary Differential Evolution Algorithm with a Case Study. J. Comput. Theor. Nanosci., 13: 7909-7921.
  28. El-Qulity, S.A. and A.W. Mohamed, 2016. A generalized national planning approach for admission capacity in higher education: a nonlinear integer goal programming model with a novel differential evolution algorithm. Comput. Intell. Neurosci., Vol. 2016. 10.1155/2016/5207362.
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  29. El-Quliti, S.A., A.W. Mohamed, R. Abdelaal and A. Bafil, 2016. A Multistage Procedure for Optimal Distribution of Preparatory-Year Students to Faculties and Departments: A Mixed Integer Nonlinear Goal Programming Model With Enhanced Differential Evolution Algorithm. J. Comput. Theor. Nanosci., 13: 7847-7863.
  30. El-Quliti, S.A., A.H.M. Ragab, R. Abdelaal and A.W. Mohamed et al., 2016. Higher Education Admission Capacity Planning Using a Large Scale Nonlinear Integer Goal Programming Model with Improved Differential Evolution Algorithm. J. Comput. Theor. Nanosci., 13: 7864-7878.
  31. Mohamed, A.W., 2015. An improved differential evolution algorithm with triangular mutation for global numerical optimization. Comput. Ind. Eng., 85: 359-375.
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  32. El-Quliti, S.A., A.H.M. Ragab, R. Abdelaal, A.W. Mohamed, A.S. Mashat, A.Y. Noaman and A.H. Altalhi, 2015. A nonlinear goal programming model for university admission capacity planning with modified differential evolution algorithm. Math. Prob. Eng., Vol. 2015. 10.1155/2015/892937.
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  33. Mohamed, A.W., 2014. RDEL: Restart Differential Evolution algorithm with Local Search Mutation for global numerical optimization. Egypt. Inf. J., 15: 175-188.
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  34. Mohamed, A.W., H.Z. Sabry and T. Abd-Elaziz, 2013. Real parameter optimization by an effective differential evolution algorithm. Egypt. Inform. J., 14: 37-53.
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  35. Mohamed, A.W., H.Z. Sabry and M. Khorshid, 2012. An alternative differential evolution algorithm for global optimization. J. Adv. Res., 3: 149-165.
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  36. Mohamed, A.W. and H.Z. Sabry, 2012. Constrained optimization based on modified differential evolution algorithm. Inform. Sci., 194: 171-208.
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  37. Wagdy, A., H. Zaher and M. Khorshid, 2011. A particle swarm approach for solving stochastic optimization problems. Applied Math. Inform. Sci. Int. J., 5: 379-401.