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Tytuł:
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A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA).
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Autorzy:
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Jia YH; College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.
Qiu J; State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing 100084, China.; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China.
Ma ZZ; State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing 100084, China.
Li FF; College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.
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Źródło:
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Computational intelligence and neuroscience [Comput Intell Neurosci] 2021 May 22; Vol. 2021, pp. 6686826. Date of Electronic Publication: 2021 May 22 (Print Publication: 2021).
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: New York, NY : Hindawi Pub. Corp.
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MeSH Terms:
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Crows*
Algorithms ; Animals ; Benchmarking ; Humans
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References:
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ISA Trans. 2020 Apr;99:210-230. (PMID: 31515097)
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Entry Date(s):
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Date Created: 20210607 Date Completed: 20210728 Latest Revision: 20210728
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Update Code:
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20240104
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PubMed Central ID:
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PMC8164537
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DOI:
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10.1155/2021/6686826
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PMID:
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34093700
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The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.
Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper.
(Copyright © 2021 Ying-Hui Jia et al.)
