Dynamic Levy Flight Chimp Optimization

The Chimp Optimization Algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents’ insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation. This paper introduces a...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Knowledge-based systems Ročník 235; s. 107625
Hlavní autoři: Kaidi, Wei, Khishe, Mohammad, Mohammadi, Mokhtar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 10.01.2022
Elsevier Science Ltd
Témata:
Algorithms
Benchmarks
Chimp Optimization Algorithm
Dynamic search
Exploitation
Hunting
Lévy Flight
Optimization
Optimization algorithms
Primates
Stagnation
State of the art
Swarm-intelligence
Winners
World problems
Swarm-intelligence
Dynamic search
Lévy Flight
Optimization
Chimp Optimization Algorithm
ISSN:0950-7051, 1872-7409
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The Chimp Optimization Algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents’ insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation. This paper introduces a Dynamic Lévy Flight (DLF) technique to smoothly and gradually transit the search agents from the exploration phase to the exploitation phase. To investigate the efficiency of the DLFChOA, this paper evaluates the performance of DLFChOA on twenty-three standard benchmark functions, twenty challenging functions of CEC-2005, ten suit tests of IEEE CEC06-2019, and twelve real-world optimization problems. The results are compared to benchmark optimization algorithms, including CMA-ES, SHADE, ChOA, HGSO, LGWO and ALEP (as the best benchmark Lévy-based algorithms), and eighteen state-of-the-art algorithms (as the winners of the CEC2019, the GECCO2019, and the SEMCCO2019). Among forty-three numerical test functions, DLFChOA and CMA-ES gain the first and second rank with thirty and eleven best results. In the 100-digit challenge, jDE100 with a score of 100 provides the best results, followed by DISHchain1e+12, and DLFChOA with a score of 85.68 is ranked fifth among eighteen state-of-the-art algorithms achieved the best score in seven out of ten problems. Finally, DLFChOA and CMA-ES respectively gain the best results in five and four real-world engineering problems.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107625