M
Mostafa Z. Ali
Researcher at Jordan University of Science and Technology
Publications - 80
Citations - 2246
Mostafa Z. Ali is an academic researcher from Jordan University of Science and Technology. The author has contributed to research in topics: Population & Optimization problem. The author has an hindex of 22, co-authored 65 publications receiving 1455 citations. Previous affiliations of Mostafa Z. Ali include Wayne State University & University of Jordan.
Papers
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Proceedings ArticleDOI
Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems
TL;DR: The proposed algorithm, namely LSHADE-cnEpSin, is tested on the IEEE CEC2017 problems used in the Special Session and Competitions on Single Objective Bound Constrained Real-Parameter Single Objective Optimization and statistically affirm the efficiency of the proposed approach.
Journal ArticleDOI
A test-suite of non-convex constrained optimization problems from the real-world and some baseline results
Abhishek Kumar,Guohua Wu,Mostafa Z. Ali,Rammohan Mallipeddi,Ponnuthurai Nagaratnam Suganthan,Swagatam Das +5 more
TL;DR: A set of 57 real-world Constrained Optimization Problems are described and presented as a benchmark suite to validate the COPs and reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.
Proceedings ArticleDOI
An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems
TL;DR: The proposed algorithm, namely LSHADE-EpSin, uses a new ensemble sinusoidal approach to automatically adapt the values of the scaling factor of the Differential Evolution algorithm to obtain better results compared to L-SHADE algorithm and other state-of-the-art algorithms.
Journal ArticleDOI
Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization
TL;DR: This paper introduces a multi-population DE to solve large-scale global optimization problems and shows that mDE-bES has a competitive performance and scalability behavior compared to the contestant algorithms.
Journal ArticleDOI
Population topologies for particle swarm optimization and differential evolution
TL;DR: A comprehensive review of population topologies developed for PSO and DE is carried out and it is anticipated that this survey will inspire researchers to integrate the populationTopologies into other nature inspired algorithms and to develop novel population topology for improving the performances of population-based optimization algorithms for solving single objective optimization, multiobjective optimization and other classes of optimization problems.