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

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...

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Vydané v:Multimedia tools and applications Ročník 83; číslo 11; s. 32613 - 32653
Hlavní autori: Abualigah, Laith, Oliva, Diego, Jia, Heming, Gul, Faiza, Khodadadi, Nima, Hussien, Abdelazim G, Shinwan, Mohammad Al, Ezugwu, Absalom E., Abuhaija, Belal, Zitar, Raed Abu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.03.2024
Springer Nature B.V
Springer Verlag
Predmet:
Algorithms
Benchmarks
Computer Communication Networks
Computer Science
Convergence
Data Structures and Information Theory
Evolution
Mathematical functions
Methods
Multimedia
Multimedia Information Systems
Optimization
Optimization algorithms
Optimization techniques
Parameter identification
Physics
Prairie dogs
Search methods
Search process
Snakes
Special Purpose and Application-Based Systems
Benchmark functions
Prairie dog optimization algorithm
Optimization problems
Dwarf mongoose optimization algorithm
Meta-heuristics
ISSN:1573-7721, 1380-7501, 1573-7721
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16890-w