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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.
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2023 (engelsk)Inngår i: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 83, nr 11, s. 32613-32653Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
SPRINGER , 2023. Vol. 83, nr 11, s. 32613-32653
Emneord [en]
Prairie dog optimization algorithm; Dwarf mongoose optimization algorithm; Meta-heuristics; Benchmark functions; Optimization problems
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Identifikatorer
URN: urn:nbn:se:liu:diva-198502DOI: 10.1007/s11042-023-16890-wISI: 001069968300003OAI: oai:DiVA.org:liu-198502DiVA, id: diva2:1805268
Tilgjengelig fra: 2023-10-16 Laget: 2023-10-16 Sist oppdatert: 2024-04-12

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