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Anas A. Hadi
Researcher at King Abdulaziz University
Publications - 18
Citations - 1008
Anas A. Hadi is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Optimization problem & Benchmark (computing). The author has an hindex of 8, co-authored 18 publications receiving 378 citations. Previous affiliations of Anas A. Hadi include Cairo University.
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Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm
TL;DR: Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions.
Proceedings ArticleDOI
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems
TL;DR: Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, of both LSHade-SPA and LSHADE-SPACMA are better than LSHades algorithm, especially as the dimension increases.
Journal ArticleDOI
Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization
TL;DR: Two proposed mutation strategies, ord_best and ord_pbest, two DE variants are introduced as EDE and EBDE, respectively and can be combined with DE family algorithms to enhance their search capabilities on difficult and complicated optimization problems.
Proceedings ArticleDOI
Evaluating the Performance of Adaptive GainingSharing Knowledge Based Algorithm on CEC 2020 Benchmark Problems
TL;DR: The key idea in this work is to extend and improve the original GSK algorithm by proposing adaptive settings to the two important control parameters: knowledge factor and knowledge ratio to control junior and senior gaining and sharing phases between the solutions during the optimization loop.
Journal ArticleDOI
LSHADE-SPA memetic framework for solving large-scale optimization problems
TL;DR: A new memetic framework for solving large-scale global optimization problems is proposed, and success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration and local exploitation.