A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm
In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evol...
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| Published in | IEEE transactions on cybernetics Vol. 52; no. 9; pp. 9290 - 9301 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2168-2267 2168-2275 2168-2275 |
| DOI | 10.1109/TCYB.2020.3029748 |
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| Abstract | In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems. |
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| AbstractList | In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems. In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems. |
| Author | Zeng, Nianyin Zhang, Hong Liu, Weibo Wang, Zidong Liu, Xiaohui Hone, Kate |
| Author_xml | – sequence: 1 givenname: Nianyin orcidid: 0000-0002-6957-2942 surname: Zeng fullname: Zeng, Nianyin email: zny@xmu.edu.cn organization: Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China – sequence: 2 givenname: Zidong orcidid: 0000-0002-9576-7401 surname: Wang fullname: Wang, Zidong email: zidong.wang@brunel.ac.uk organization: Department of Computer Science, Brunel University London, Uxbridge, U.K – sequence: 3 givenname: Weibo orcidid: 0000-0002-8169-3261 surname: Liu fullname: Liu, Weibo organization: Department of Computer Science, Brunel University London, Uxbridge, U.K – sequence: 4 givenname: Hong surname: Zhang fullname: Zhang, Hong organization: Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China – sequence: 5 givenname: Kate surname: Hone fullname: Hone, Kate organization: Department of Computer Science, Brunel University London, Uxbridge, U.K – sequence: 6 givenname: Xiaohui surname: Liu fullname: Liu, Xiaohui organization: Department of Computer Science, Brunel University London, Uxbridge, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33170793$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Acceleration Algorithms Convergence Differential evolution (DE) dynamic neighborhood Evolutionary algorithms Evolutionary computation Heuristic algorithms Iterative methods Neighborhoods Optimization Particle swarm optimization particle swarm optimization (PSO) Performance evaluation Search problems Switches Switching switching strategy Topology |
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| Title | A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm |
| URI | https://ieeexplore.ieee.org/document/9254137 https://www.ncbi.nlm.nih.gov/pubmed/33170793 https://www.proquest.com/docview/2704099079 https://www.proquest.com/docview/2459623739 http://bura.brunel.ac.uk/bitstream/2438/21856/1/FullText.pdf |
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