Publikationer från Malmö universitet
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies
Jishou Univ, Sch Commun & Elect Engn, Jishou 416000, Hunan, Peoples R China.;Jishou Univ, Lab Ethn Cultural Heritage Digitizat Wuling Mt Ar, Jishou 416000, Hunan, Peoples R China..
Jishou Univ, Sch Commun & Elect Engn, Jishou 416000, Hunan, Peoples R China.;Jishou Univ, Lab Ethn Cultural Heritage Digitizat Wuling Mt Ar, Jishou 416000, Hunan, Peoples R China..ORCID-id: 0000-0001-5779-7135
Jishou Univ, Sch Commun & Elect Engn, Jishou 416000, Hunan, Peoples R China.;Jishou Univ, Lab Ethn Cultural Heritage Digitizat Wuling Mt Ar, Jishou 416000, Hunan, Peoples R China..
Jishou Univ, Sch Commun & Elect Engn, Jishou 416000, Hunan, Peoples R China.;Jishou Univ, Lab Ethn Cultural Heritage Digitizat Wuling Mt Ar, Jishou 416000, Hunan, Peoples R China..
Vise andre og tillknytning
2022 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 10, s. 96159-96179Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2022. Vol. 10, s. 96159-96179
Emneord [en]
Statistics, Sociology, Optimization, Chaos, Standards, Search problems, Convergence, Adaptive weighting modification, cubic chaos mapping, levy flight, reverse learning, sparrow search algorithm
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-55407DOI: 10.1109/ACCESS.2022.3204798ISI: 000857703700001Scopus ID: 2-s2.0-85137937933OAI: oai:DiVA.org:mau-55407DiVA, id: diva2:1704177
Tilgjengelig fra: 2022-10-17 Laget: 2022-10-17 Sist oppdatert: 2024-02-05bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Person

Sarkheyli-Hägele, Arezoo

Søk i DiVA

Av forfatter/redaktør
Zhou, Kai-QingSarkheyli-Hägele, Arezoo
Av organisasjonen
I samme tidsskrift
IEEE Access

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 63 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf