liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems
Al al Bayt Univ, Jordan; Al Ahliyya Amman Univ, Jordan; Lebanese Amer Univ, Lebanon; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ Malaysia, Malaysia.
Univ Guadalajara, Mexico.
Sanming Univ, Peoples R China.
Air Univ, Pakistan.
Show others and affiliations
2023 (English)In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 83, no 11, p. 32613-32653Article in journal (Refereed) Published
Abstract [en]

Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.

Place, publisher, year, edition, pages
SPRINGER , 2023. Vol. 83, no 11, p. 32613-32653
Keywords [en]
Prairie dog optimization algorithm; Dwarf mongoose optimization algorithm; Meta-heuristics; Benchmark functions; Optimization problems
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-198502DOI: 10.1007/s11042-023-16890-wISI: 001069968300003OAI: oai:DiVA.org:liu-198502DiVA, id: diva2:1805268
Available from: 2023-10-16 Created: 2023-10-16 Last updated: 2024-04-12

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Hussien, Abdelazim
By organisation
Software and SystemsFaculty of Science & Engineering
In the same journal
Multimedia tools and applications
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 50 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf