Journal Article10.1007/S00500-021-05889-W
Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems
Mohammed A. A. Al-qaness,Ahmed A. Ewees,Ahmed A. Ewees,Mohamed Abd Elaziz,Mohamed Abd Elaziz +4 more
- 01 Jul 2021
- Vol. 25, Iss: 14, pp 9545-9557
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Citations
Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model
Ahmed A. Ewees,Mohammed A. A. Al-qaness,Laith Abualigah,Diego Oliva,Zakariya Yahya Algamal,Ahmed M. Anter,Rehab Ali Ibrahim,Rania M. Ghoniem,Mohamed Abd Elaziz +8 more
- 19 Sep 2021
TL;DR: The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.
67
Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
Mohamed Abd Elaziz,Laith Abualigah,Dalia Yousri,Diego Oliva,Mohammed A. A. Al-qaness,Mohammad H. Nadimi-Shahraki,Ahmed A. Ewees,Songfeng Lu,Rehab Ali Ibrahim +8 more
- 03 Nov 2021
TL;DR: In this paper, a modified version of new metaheuristic techniques named Atomic Orbital Search (AOS) as FS technique is presented using the advances of dynamic opposite-based learning strategy that is used to enhance the ability of AOS to explore the search domain.
14
•Posted Content
Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey.
TL;DR: In this paper, an extensive literature review on the application of heuristic and metaheuristic methods for solving the unrelated parallel machines scheduling problem (UPMSP) is provided. But no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP.
3
Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
Abdulaziz Fatani,Abdelghani Dahou,Mohammed A. A. Al-qaness,Songfeng Lu,Mohamed Abd Elaziz +4 more
TL;DR: This study develops a deep learning-based intrusion detection system for IoT using Aquila optimizer for feature extraction and selection, achieving high performance on four public datasets with competitive results compared to other optimization methods.
References
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
11.1K
•Book
Nature-Inspired Metaheuristic Algorithms
Xin-She Yang
- 01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
4.9K
Firefly algorithms for multimodal optimization
Xin-She Yang
- 26 Oct 2009
TL;DR: In this article, a new Firefly Algorithm (FA) was proposed for multimodal optimization applications. And the proposed FA was compared with other metaheuristic algorithms such as particle swarm optimization (PSO).
Hybrid Whale Optimization Algorithm with Simulated Annealing for Feature Selection
Majdi Mafarja,Seyedali Mirjalili +1 more
TL;DR: The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.
1.1K
Scheduling with Deadlines and Loss Functions
TL;DR: The problem of this paper is that of scheduling several one-stage tasks on several processors, which are capable of handling the tasks with varying degrees of efficiency, to minimize the total loss, which is a sum of losses associated with the individual tasks.
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