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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Main Authors: Hashim, Ahmad Affendi, Abdullah, Rosni
Format: Conference Object
Language:unknown
Published: Zenodo 2019
Subjects:
Canis lupus
Online Access:https://dx.doi.org/10.5281/zenodo.3474212
https://zenodo.org/record/3474212
id ftdatacite:10.5281/zenodo.3474212
record_format openpolar
spelling ftdatacite:10.5281/zenodo.3474212 2023-05-15T15:50:18+02:00 AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE Hashim, Ahmad Affendi Abdullah, Rosni 2019 https://dx.doi.org/10.5281/zenodo.3474212 https://zenodo.org/record/3474212 unknown Zenodo https://zenodo.org/communities/cspc-2018 https://dx.doi.org/10.5281/zenodo.3474213 https://zenodo.org/communities/cspc-2018 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Text Conference paper article-journal ScholarlyArticle 2019 ftdatacite https://doi.org/10.5281/zenodo.3474212 https://doi.org/10.5281/zenodo.3474213 2021-11-05T12:55:41Z 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 DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
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 Hashim, Ahmad Affendi
Abdullah, Rosni
spellingShingle Hashim, Ahmad Affendi
Abdullah, Rosni
AN IMPROVED GREY WOLF OPTIMIZER WITH LVY WALK AS AN OPTIMIZER IN TRAINING MULTI-LAYER PERCEPTRON WITH DECOUPLED NEURAL INTERFACE
author_facet Hashim, Ahmad Affendi
Abdullah, Rosni
author_sort Hashim, Ahmad Affendi
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://dx.doi.org/10.5281/zenodo.3474212
https://zenodo.org/record/3474212
genre Canis lupus
genre_facet Canis lupus
op_relation https://zenodo.org/communities/cspc-2018
https://dx.doi.org/10.5281/zenodo.3474213
https://zenodo.org/communities/cspc-2018
op_rights Open Access
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
info:eu-repo/semantics/openAccess
op_rightsnorm CC-BY
op_doi https://doi.org/10.5281/zenodo.3474212
https://doi.org/10.5281/zenodo.3474213
_version_ 1766385276493496320

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