Hybrid Whale Optimization Algorithm with simulated annealing for feature selection

By Majdi M. Mafarja, Seyedali Mirjalili. Neurocomputing, 2017

1,090 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0705fddf9f22d12c3240c705cc95df89c1dd6827

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tansel Dökeroğlu
2. Ali Asghar Heidari
3. Huiling Chen
4. Ayça Deniz
5. Hakan Ezgi Kiziloz
6. Ali Wagdy Mohamed
7. Talari Ganesh
8. Hattan F. Abutarboush

In Computational Theory and Mathematics

1. Tansel Dökeroğlu
2. Ali Asghar Heidari
3. Ayça Deniz
4. Hakan Ezgi Kiziloz
5. Huiling Chen
6. Hossam Faris
7. Ibrahim Aljarah
8. Ali Wagdy Mohamed

Citations in the corpus

• The Social Engineering Optimizer (SEO) (2018, neutral): “Accordingly, the performance of the proposed algorithm is explored by a well-known problem in TP, namely, Vehicle Routing Problem (VRP).”
• A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “Mirjalili et al. proposed the SSA in 2017 [48] .”
• Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, methodology): “In order to achieve a good balance, one option is to utilize a hybrid approach where two algorithms or more are combined to improve each algorithm’s performance and the resulted hybrid approach is named a memetic method [18].”
• An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “The Whale Optimization Algorithm (WOA)-based FS approaches has also been proposed in [49], in which different hybridization models between the WOA and Simulated Annealing (SA) algorithm have been proposed for FS problems.”
• Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “Other than, Mafarja and Mirjalili [187] embedded two rough set filter approach Quick Reduct and CEBARKCC with the ALO algorithm, which improved the initial population as well as the final optimal solution.”
• Boosted binary Harris hawks optimizer and feature selection (2020, methodology): “Moreover, there are also some hybridization optimizers in the FS techniques [49, 68–70].”