Differential Evolution in Wireless Communications: A Review
<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless com...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
International Association of Online Engineering (IAOE)
2019-07-01
|
Series: | International Journal of Online and Biomedical Engineering |
Subjects: | |
Online Access: | https://online-journals.org/index.php/i-joe/article/view/10651 |
id |
doaj-83a8518b62b24f5887eb969f81b6ef54 |
---|---|
record_format |
Article |
spelling |
doaj-83a8518b62b24f5887eb969f81b6ef542021-09-02T08:30:34ZengInternational Association of Online Engineering (IAOE)International Journal of Online and Biomedical Engineering2626-84932019-07-011511295210.3991/ijoe.v15i11.106514593Differential Evolution in Wireless Communications: A ReviewHilary I OkagbueMuminu O AdamuTimothy A Anake<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.</p>https://online-journals.org/index.php/i-joe/article/view/10651Differential evolutionmultiobjective optimizationevolutionary computationenergy utilizationlocalizationcoveragewireless networks. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hilary I Okagbue Muminu O Adamu Timothy A Anake |
spellingShingle |
Hilary I Okagbue Muminu O Adamu Timothy A Anake Differential Evolution in Wireless Communications: A Review International Journal of Online and Biomedical Engineering Differential evolution multiobjective optimization evolutionary computation energy utilization localization coverage wireless networks. |
author_facet |
Hilary I Okagbue Muminu O Adamu Timothy A Anake |
author_sort |
Hilary I Okagbue |
title |
Differential Evolution in Wireless Communications: A Review |
title_short |
Differential Evolution in Wireless Communications: A Review |
title_full |
Differential Evolution in Wireless Communications: A Review |
title_fullStr |
Differential Evolution in Wireless Communications: A Review |
title_full_unstemmed |
Differential Evolution in Wireless Communications: A Review |
title_sort |
differential evolution in wireless communications: a review |
publisher |
International Association of Online Engineering (IAOE) |
series |
International Journal of Online and Biomedical Engineering |
issn |
2626-8493 |
publishDate |
2019-07-01 |
description |
<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.</p> |
topic |
Differential evolution multiobjective optimization evolutionary computation energy utilization localization coverage wireless networks. |
url |
https://online-journals.org/index.php/i-joe/article/view/10651 |
work_keys_str_mv |
AT hilaryiokagbue differentialevolutioninwirelesscommunicationsareview AT muminuoadamu differentialevolutioninwirelesscommunicationsareview AT timothyaanake differentialevolutioninwirelesscommunicationsareview |
_version_ |
1721177763495280640 |