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An Artificial Bee Colony Algorithm with Dual Groups and Multiple Strategies Based on Reinforcement Learning.

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Τίτλος: An Artificial Bee Colony Algorithm with Dual Groups and Multiple Strategies Based on Reinforcement Learning.
Συγγραφείς: Cao, Yang1,2,3 (AUTHOR), Li, Zilin1,2,3 (AUTHOR) lizilin@stu.sjzu.edu.cn
Πηγή: Mathematics (2227-7390). Jul2026, Vol. 14 Issue 13, p2276. 30p.
Θεματικοί όροι: *Bees algorithm, *Reinforcement learning, *Benchmark problems (Computer science), *Swarm intelligence, *Optimization algorithms, *Metaheuristic algorithms
Περίληψη: The Artificial Bee Colony (ABC) algorithm is widely used for continuous optimization, but the standard ABC still suffers from insufficient use of neighborhood information, limited adaptability of search behavior, and random restarts in the scout bee phase, which may lead to slow convergence and reduced solution quality on complex problems. To address these limitations, this paper proposes an artificial bee colony algorithm with dual groups and multiple strategies based on reinforcement learning, named RLDMS-ABC. In the employed bee phase, Q-learning is used to adaptively adjust the neighborhood size of each food source according to search feedback, and the best individual selected from the sampled neighborhood guides candidate solution generation. In the onlooker bee phase, selected food sources are divided into elite and ordinary groups according to relative quality, and different search strategies are assigned to balance exploration and exploitation. In the scout bee phase, a guided restart mechanism combining opposition-based learning and the current global best solution is designed to help stagnated individuals escape local optima. Experiments on the CEC 2014 benchmark set show that RLDMS-ABC outperforms several representative ABC variants on most functions in terms of solution quality, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]
Βάση Δεδομένων: Academic Search Index
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  Data: An Artificial Bee Colony Algorithm with Dual Groups and Multiple Strategies Based on Reinforcement Learning.
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  Data: <searchLink fieldCode="AR" term="%22Cao%2C+Yang%22">Cao, Yang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zilin%22">Li, Zilin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> lizilin@stu.sjzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Mathematics+%282227-7390%29%22">Mathematics (2227-7390)</searchLink>. Jul2026, Vol. 14 Issue 13, p2276. 30p.
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  Data: *<searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br />*<searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The Artificial Bee Colony (ABC) algorithm is widely used for continuous optimization, but the standard ABC still suffers from insufficient use of neighborhood information, limited adaptability of search behavior, and random restarts in the scout bee phase, which may lead to slow convergence and reduced solution quality on complex problems. To address these limitations, this paper proposes an artificial bee colony algorithm with dual groups and multiple strategies based on reinforcement learning, named RLDMS-ABC. In the employed bee phase, Q-learning is used to adaptively adjust the neighborhood size of each food source according to search feedback, and the best individual selected from the sampled neighborhood guides candidate solution generation. In the onlooker bee phase, selected food sources are divided into elite and ordinary groups according to relative quality, and different search strategies are assigned to balance exploration and exploitation. In the scout bee phase, a guided restart mechanism combining opposition-based learning and the current global best solution is designed to help stagnated individuals escape local optima. Experiments on the CEC 2014 benchmark set show that RLDMS-ABC outperforms several representative ABC variants on most functions in terms of solution quality, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/math14132276
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 30
        StartPage: 2276
    Subjects:
      – SubjectFull: Bees algorithm
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Benchmark problems (Computer science)
        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
    Titles:
      – TitleFull: An Artificial Bee Colony Algorithm with Dual Groups and Multiple Strategies Based on Reinforcement Learning.
        Type: main
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          Name:
            NameFull: Cao, Yang
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          Name:
            NameFull: Li, Zilin
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          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 22277390
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              Value: 14
            – Type: issue
              Value: 13
          Titles:
            – TitleFull: Mathematics (2227-7390)
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