N
Nailah Al-Madi
Researcher at Princess Sumaya University for Technology
Publications - 22
Citations - 528
Nailah Al-Madi is an academic researcher from Princess Sumaya University for Technology. The author has contributed to research in topics: Artificial neural network & Evolutionary computation. The author has an hindex of 10, co-authored 22 publications receiving 344 citations. Previous affiliations of Nailah Al-Madi include North Dakota State University.
Papers
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Journal ArticleDOI
Training radial basis function networks using biogeography-based optimizer
TL;DR: The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima.
Journal ArticleDOI
Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm
TL;DR: The quantitative and qualitative results show that the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed.
Journal ArticleDOI
A dynamic locality multi-objective salp swarm algorithm for feature selection
Ibrahim Aljarah,Maria Habib,Hossam Faris,Nailah Al-Madi,Ali Asghar Heidari,Ali Asghar Heidari,Majdi Mafarja,Mohamed Abd Elaziz,Seyedali Mirjalili +8 more
TL;DR: An enhanced multi-objective SSA algorithm that adopts two essential components: the dynamic time-varying strategy and local fittest solutions that assist the SSAgorithm in balancing exploration and exploitation and converges faster while avoiding locally optimal solutions is proposed.
Journal ArticleDOI
Binary multi-verse optimization algorithm for global optimization and discrete problems
TL;DR: A binary version of Multi-verse optimizer is proposed, equipped with a V-shaped transfer function to covert continuous values to binary, and update the solutions over the course of optimization to solve problems with discrete variables such as feature selection and knapsack problems.
Proceedings ArticleDOI
Parallel glowworm swarm optimization clustering algorithm based on MapReduce
TL;DR: A scalable design and implementation of glowworm swarm optimization clustering (MRCGSO) using MapReduce is introduced to handle big data and achieves a very close to linear speedup while maintaining the clustering quality.