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Biogeography in optimization algorithms: a closer look

IAES International Journal of Artificial Intelligence · Vol. 10 No. 4 (2021) · hlm. 982–989

DOI
10.11591/ijai.v10.i4.pp982-989
ISSN
2252-8938, 2089-4872
Terbit
2021-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Biogeography can be broken down into bio and geography, which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology inspired algorithm is inclined towards providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, and some for the worse. This paper tries to mimic the natural influences in a habitat in a python environment and try to come up with a minimal objective value after iterating through the given metaheuristic algorithm.

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  • International Institute of Information Technology
  • Woosong University
  • KIIT University
  • Veer Surendra Sai University of Technology
  • Biju Patnaik University of Technology
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Rekan penulisOrganisasiArtikel bersama
Rudranarayan SenapatiKIIT University9
Sivkumar MishraBiju Patnaik University of Technology6
Seong-Cheol KimWoosong University4
Papia RayVeer Surendra Sai University of Technology4
Arun Kumar SahooInternational Institute of Information Technology4
Rambilli Krishna Prasad Rao NaiduInternational Institute of Information Technology4
S. NarasimhaAfiliasi tidak diketahui3
Sanchita SahayInternational Institute of Information Technology3
Kaushiki AgrawalInternational Institute of Information Technology3
Aditya Prasad MahapatraInternational Institute of Information Technology2
Bishweashwar SuklaInternational Institute of Information Technology2
K. M. HarikrishnanInternational Institute of Information Technology2
Ayush KumarInternational Institute of Information Technology2
Sandip Ranjan BeheraInternational Institute of Information Technology2
Arul Kumar DashInternational Institute of Information Technology2
Prajna Jeet OjhaInternational Institute of Information Technology2
Lingam YashwanthInternational Institute of Information Technology2
Peesodi UdayInternational Institute of Information Technology2
Bhabesh RoutrayInternational Institute of Information Technology2
Nitesh PattanayakInternational Institute of Information Technology2
Priyansu ShekharInternational Institute of Information Technology2
Pratyus Ranjan BeheraInternational Institute of Information Technology2
Priyanka raiInternational Institute of Information Technology2
Prince KumarInternational Institute of Information Technology2
Ayush KumarInternational Institute of Information Technology2
Prabin BiswalInternational Institute of Information Technology2
Swayamjyoti SatapathyInternational Institute of Information Technology2
Smruti Susmita SahuInternational Institute of Information Technology2
Chan-Mook JungWoosong University1
Pankaj SharmaWoosong University1
Monalisa PandaInternational Institute of Information Technology1
Debani Prashad MishraInternational Institute of Information Technology1
Sopa Mousumi PatroInternational Institute of Information Technology1
Kalpa Ranjan BeheraWoosong University1
Debani Prasad MishraInternational Institute of Information Technology1
Raj Alpha SwainInternational Institute of Information Technology1
Nisha KediaInternational Institute of Information Technology1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Biogeography in optimization algorithms: a closer look</dc:title>
	<dc:creator>Palai, Padarabinda</dc:creator>
	<dc:creator>Mishra, Debani Prasad</dc:creator>
	<dc:creator>Salkuti, Surender Reddy</dc:creator>
	<dc:subject xml:lang="en-US">Artificial Intelligence; Meta-heuristic algorithms; Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Biogeography, Immigration rate, Migration, Nature inspired algorithms, Optimization</dc:subject>
	<dc:description xml:lang="en-US">Biogeography can be broken down into bio and geography, which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology inspired algorithm is inclined towards providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, and some for the worse. This paper tries to mimic the natural influences in a habitat in a python environment and try to come up with a minimal objective value after iterating through the given metaheuristic algorithm.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Woosong University's Academic Research Funding - (2020-2021)</dc:contributor>
	<dc:date>2021-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20858</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i4.pp982-989</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 4: December 2021; 982-989</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20858/13248</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>