AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE
Grey Wolf Optimizer (GWO) is inspired by how grey wolves (Canis Lupus) searching its prey. The GWO relatively new swarm-based intelli- gence and the only algorithms that are based on the leadership hierarchy. In GWO, four types of grey wolves such as alpha, beta, delta and omega are employed simulat...
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ftzenodo:oai:zenodo.org:3474213 2024-09-15T18:01:15+00:00 AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE Ahmad Affendi Hashim Abdullah, Rosni 2019-10-06 https://doi.org/10.5281/zenodo.3474213 unknown Zenodo https://zenodo.org/communities/cspc-2018 https://doi.org/10.5281/zenodo.3474212 https://doi.org/10.5281/zenodo.3474213 oai:zenodo.org:3474213 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/conferencePaper 2019 ftzenodo https://doi.org/10.5281/zenodo.347421310.5281/zenodo.3474212 2024-07-26T18:25:47Z Grey Wolf Optimizer (GWO) is inspired by how grey wolves (Canis Lupus) searching its prey. The GWO relatively new swarm-based intelli- gence and the only algorithms that are based on the leadership hierarchy. In GWO, four types of grey wolves such as alpha, beta, delta and omega are employed simulating the leadership hierarchy. Additionally, there are three main steps of hunt- ing, searching for prey, encircling prey and at- tacking prey are implemented. To improve the GWO search ability, this study proposed Lvy - GWO based on Lvy walk. Five well define bench- mark functions were selected in this study. The five benchmark functions were selected based on its features that have many local minima. The results indicate that Lvy -GWO did improve the original GWO based on the error value. Based on Lvy - GWO algorithm. It will be then pro- posed serving as an optimizer in training multi- layer perceptron (MLP) with Decouple Neural In- terface (DNI). Conference Object Canis lupus Zenodo |
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description |
Grey Wolf Optimizer (GWO) is inspired by how grey wolves (Canis Lupus) searching its prey. The GWO relatively new swarm-based intelli- gence and the only algorithms that are based on the leadership hierarchy. In GWO, four types of grey wolves such as alpha, beta, delta and omega are employed simulating the leadership hierarchy. Additionally, there are three main steps of hunt- ing, searching for prey, encircling prey and at- tacking prey are implemented. To improve the GWO search ability, this study proposed Lvy - GWO based on Lvy walk. Five well define bench- mark functions were selected in this study. The five benchmark functions were selected based on its features that have many local minima. The results indicate that Lvy -GWO did improve the original GWO based on the error value. Based on Lvy - GWO algorithm. It will be then pro- posed serving as an optimizer in training multi- layer perceptron (MLP) with Decouple Neural In- terface (DNI). |
format |
Conference Object |
author |
Ahmad Affendi Hashim Abdullah, Rosni |
spellingShingle |
Ahmad Affendi Hashim Abdullah, Rosni AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE |
author_facet |
Ahmad Affendi Hashim Abdullah, Rosni |
author_sort |
Ahmad Affendi Hashim |
title |
AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE |
title_short |
AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE |
title_full |
AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE |
title_fullStr |
AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE |
title_full_unstemmed |
AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE |
title_sort |
improved grey wolf optimizer with lvy walk as an optimizer in training multi-layer perceptron with decoupled neural interface |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://doi.org/10.5281/zenodo.3474213 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_relation |
https://zenodo.org/communities/cspc-2018 https://doi.org/10.5281/zenodo.3474212 https://doi.org/10.5281/zenodo.3474213 oai:zenodo.org:3474213 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.347421310.5281/zenodo.3474212 |
_version_ |
1810438417119444992 |