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Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems

Abualigah, Laith (author)
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
Oliva, Diego (author)
Univ Guadalajara, Mexico
Jia, Heming (author)
Sanming Univ, Peoples R China
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Gul, Faiza (author)
Air Univ, Pakistan
Khodadadi, Nima (author)
Florida Int Univ, FL USA
Hussien, Abdelazim (author)
Linköpings universitet,Programvara och system,Tekniska fakulteten,Fayoum Univ, Egypt
Al Shinwan, Mohammad (author)
Appl Sci Private Univ, Jordan
Ezugwu, Absalom E. (author)
North West Univ, South Africa
Abuhaija, Belal (author)
Wenzhou Kean Univ, Peoples R China
Abu Zitar, Raed (author)
Sorbonne Univ Abu Dhabi, U Arab Emirates
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 (creator_code:org_t)
SPRINGER, 2023
2023
English.
In: Multimedia tools and applications. - : SPRINGER. - 1380-7501 .- 1573-7721. ; 83:11, s. 32613-32653
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Prairie dog optimization algorithm; Dwarf mongoose optimization algorithm; Meta-heuristics; Benchmark functions; Optimization problems

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