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Dynamic group optim...
Dynamic group optimization algorithm with a mean-variance search framework
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- Tang, Rui (författare)
- Department of management science and information system, Faculty of Management and Economics, Kunming university of science and technology, China
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- Yang, Jie (författare)
- Department of Electromechanical Engineering, Chongqing Industry & Trade Polytechnic, Chongqing 408000, China; Department of Computer and Information Science, University of Macau, Taipa, Macau
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- Fong, Simon (författare)
- Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, Macau
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- Wong, Raymond (författare)
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
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- Vasilakos, Athanasios V. (författare)
- Luleå tekniska universitet,Datavetenskap,School of Electrical and Data Engineering, University of Technology Sydney, Australia; College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
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- Chen, Yu (författare)
- Department of management science and information system, Faculty of Management and Economics, Kunming university of science and technology, China
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(creator_code:org_t)
- Elsevier, 2021
- 2021
- Engelska.
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 183
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Dynamic group optimization has recently appeared as a novel algorithm developed to mimic animal and human socialising behaviours. Although the algorithm strongly lends itself to exploration and exploitation, it has two main drawbacks. The first is that the greedy strategy, used in the dynamic group optimization algorithm, guarantees to evolve a generation of solutions without deteriorating than the previous generation but decreases population diversity and limit searching ability. The second is that most information for updating populations is obtained from companions within each group, which leads to premature convergence and deteriorated mutation operators. The dynamic group optimization with a mean-variance search framework is proposed to overcome these two drawbacks, an improved algorithm with a proportioned mean solution generator and a mean-variance Gaussian mutation. The new proportioned mean solution generator solutions do not only consider their group but also are affected by the current solution and global situation. The mean-variance Gaussian mutation takes advantage of information from all group heads, not solely concentrating on information from the best solution or one group. The experimental results on public benchmark test suites show that the proposed algorithm is effective and efficient. In addition, comparative results of engineering problems in welded beam design show the promise of our algorithms for real-world applications.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- metaheuristic algorithm
- dynamic group optimization algorithm
- mean-variance search framework
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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