Publications

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Journal Article
Saha, D., K. M. Sallam, S. De, and A. W. Mohamed, CHAGSKODE algorithm for solving real world constrained optimization problems, : Preprints, 2022. Abstract
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Alrasheedi, A. F., K. A. Alnowibet, A. Saxena, K. M. Sallam, and A. W. Mohamed, "Chaos embed marine predator (CMPA) algorithm for feature selection", Mathematics, vol. 10, issue 9: MDPI, pp. 1411, 2022. Abstract
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Agrawal, P., T. Ganesh, and A. W. Mohamed, Chaotic gaining sharing knowledge-based optimization algorithm: an improved metaheuristic algorithm for feature selection, , vol. 25, issue 14, pp. 9505 - 9528, 2021. AbstractWebsite

The gaining sharing knowledge based optimization algorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates a modified version of the GSK algorithm to find the best feature subsets. Firstly, it represents a binary variant of GSK algorithm by employing a probability estimation operator (Bi-GSK) on the two main pillars of GSK algorithm. And then, the chaotic maps are used to enhance the performance of the proposed algorithm. Ten different types of chaotic maps are considered to adapt the parameters of the GSK algorithm that make a proper balance between exploration and exploitation and save the algorithm from premature convergence. To check the performance of proposed approaches of GSK algorithm, twenty-one benchmark datasets are taken from the UCI repository for feature selection. The performance is measured by calculating different type of measures, and several metaheuristic algorithms are adopted to compare the obtained results. The results indicate that Chebyshev chaotic map shows the best result among all chaotic maps which improve the performance accuracy and convergence rate of the original algorithm. Moreover, it outperforms the other metaheuristic algorithms in terms of efficiency, fitness value and the minimum number of selected features.

Prachi Agrawal, Talari Ganesh, A. W. M., "Chaotic gaining sharing knowledge-based optimization algorithm: an improved metaheuristic algorithm for feature selection", Soft Computing: Springer Link, pp. https://doi.org/10.1007/s00500-021-05874, 2021. Abstract
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Prachi Agrawal, Talari Ganesh, A. W. M., "Chaotic gaining sharing knowledge-based optimization algorithm: an improved metaheuristic algorithm for feature selection", Soft Computing: Springer Link, pp. https://doi.org/10.1007/s00500-021-05874, 2021. Abstract
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Makwe, A., P. Kanungo, S. Kautish, G. Madhu, A. S. Almazyad, G. Xiong, and A. W. Mohamed, "Cloud service prioritization using a Multi-Criteria Decision-Making technique in a cloud computing environment", Ain Shams Engineering Journal, vol. 15, issue 7: Elsevier, pp. 102785, 2024. Abstract
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Fu, L., H. Ouyang, C. Zhang, S. Li, and A. W. Mohamed, "A constrained cooperative adaptive multi-population differential evolutionary algorithm for economic load dispatch problems", Applied Soft Computing, vol. 121: Elsevier, pp. 108719, 2022. Abstract
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Mohamed, A. W., and H. Z. Sabry, "Constrained optimization based on modified differential evolution algorithm", Information Sciences, vol. 194: Elsevier, pp. 171–208, 2012. Abstract
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Mohamed, A. W., and H. Z. Sabry, "Constrained optimization based on modified differential evolution algorithm", Information Sciences, vol. 194: Elsevier, pp. 171-208, 2012. Abstract
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Mohamed, A. W., and H. Z. Sabry, "Constrained optimization based on modified differential evolution algorithm", Information Sciences, vol. 194: Elsevier, pp. 171-208, 2012. Abstract
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Mohamed, A. K., and A. W. Mohamed, "Control Parameters in Differential Evolution (DE): A Short Review", Robotics & Automation Engineering, vol. 3, issue 2: Juniper, 2018. Abstract
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Conference Proceedings
Nabeeh, N. A., A. Abdel-Monem, M. Mohamed, K. M. Sallam, M. Abdel-Basset, M. El-Abd, and A. Wagdy, "A comparative analysis for a novel hybrid methodology using neutrosophic theory with MCDM for Manufacture selection", 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): IEEE, pp. 1-8, 2022. Abstract
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