Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems
A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitati...
Published in: | Soft Computing |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Linköpings universitet, Programvara och system
2023
|
Subjects: | |
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195768 https://doi.org/10.1007/s00500-023-08468-3 |
id |
ftlinkoepinguniv:oai:DiVA.org:liu-195768 |
---|---|
record_format |
openpolar |
spelling |
ftlinkoepinguniv:oai:DiVA.org:liu-195768 2024-04-28T08:14:28+00:00 Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems Hussien, Abdelazim Abu Khurma, Ruba Alzaqebah, Abdullah Amin, Mohamed Hashim, Fatma A. 2023 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195768 https://doi.org/10.1007/s00500-023-08468-3 eng eng Linköpings universitet, Programvara och system Linköpings universitet, Tekniska fakulteten Fayoum Univ, Egypt Al Ahliyya Univ, Jordan World Islamic Sci & Educ Univ, Jordan Menoufia Univ, Egypt Helwan Univ, Egypt; Middle East Univ, Jordan SPRINGER Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1432-7643, 2023, 27, s. 13951-13989 orcid:0000-0001-5394-0678 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195768 doi:10.1007/s00500-023-08468-3 ISI:001005056400005 info:eu-repo/semantics/openAccess Beluga Whale Optimization BWO Elite Evolution Strategy Self-adaptive exploration-exploitation Engineering problems Computer Sciences Datavetenskap (datalogi) Article in journal info:eu-repo/semantics/article text 2023 ftlinkoepinguniv https://doi.org/10.1007/s00500-023-08468-3 2024-04-03T14:16:17Z A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithms performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizers optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering ... Article in Journal/Newspaper Beluga Beluga whale Beluga* LIU - Linköping University: Publications (DiVA) Soft Computing 27 19 13951 13989 |
institution |
Open Polar |
collection |
LIU - Linköping University: Publications (DiVA) |
op_collection_id |
ftlinkoepinguniv |
language |
English |
topic |
Beluga Whale Optimization BWO Elite Evolution Strategy Self-adaptive exploration-exploitation Engineering problems Computer Sciences Datavetenskap (datalogi) |
spellingShingle |
Beluga Whale Optimization BWO Elite Evolution Strategy Self-adaptive exploration-exploitation Engineering problems Computer Sciences Datavetenskap (datalogi) Hussien, Abdelazim Abu Khurma, Ruba Alzaqebah, Abdullah Amin, Mohamed Hashim, Fatma A. Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
topic_facet |
Beluga Whale Optimization BWO Elite Evolution Strategy Self-adaptive exploration-exploitation Engineering problems Computer Sciences Datavetenskap (datalogi) |
description |
A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithms performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizers optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering ... |
format |
Article in Journal/Newspaper |
author |
Hussien, Abdelazim Abu Khurma, Ruba Alzaqebah, Abdullah Amin, Mohamed Hashim, Fatma A. |
author_facet |
Hussien, Abdelazim Abu Khurma, Ruba Alzaqebah, Abdullah Amin, Mohamed Hashim, Fatma A. |
author_sort |
Hussien, Abdelazim |
title |
Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
title_short |
Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
title_full |
Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
title_fullStr |
Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
title_full_unstemmed |
Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
title_sort |
novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems |
publisher |
Linköpings universitet, Programvara och system |
publishDate |
2023 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195768 https://doi.org/10.1007/s00500-023-08468-3 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_relation |
Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1432-7643, 2023, 27, s. 13951-13989 orcid:0000-0001-5394-0678 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195768 doi:10.1007/s00500-023-08468-3 ISI:001005056400005 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1007/s00500-023-08468-3 |
container_title |
Soft Computing |
container_volume |
27 |
container_issue |
19 |
container_start_page |
13951 |
op_container_end_page |
13989 |
_version_ |
1797580515377676288 |