Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88834
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Leung, MF | - |
dc.creator | Coello, CAC | - |
dc.creator | Cheung, CC | - |
dc.creator | Ng, SC | - |
dc.creator | Lui, AKF | - |
dc.date.accessioned | 2020-12-22T01:08:18Z | - |
dc.date.available | 2020-12-22T01:08:18Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/88834 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication M. -F. Leung, C. A. C. Coello, C. -C. Cheung, S. -C. Ng and A. K. -F. Lui, "A Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization," in IEEE Access, vol. 8, pp. 189527-189545, 2020 is available at https://dx.doi.org/10.1109/ACCESS.2020.3031002 | en_US |
dc.subject | Particle swarm optimization | en_US |
dc.subject | Convergence | en_US |
dc.subject | Euclidean distance | en_US |
dc.subject | Benchmark testing | en_US |
dc.subject | Linear programming | en_US |
dc.subject | Pareto optimization | en_US |
dc.subject | Many-objective optimization | en_US |
dc.subject | Particle swarm optimization | en_US |
dc.subject | Leader selection | en_US |
dc.title | A hybrid leader selection strategy for many-objective particle swarm optimization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 189527 | - |
dc.identifier.epage | 189545 | - |
dc.identifier.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3031002 | - |
dcterms.abstract | Many existing Multi-objective Particle Swarm Optimizers (MOPSOs) may encounter difficulties for a set of good approximated solutions when solving problems with more than three objectives. One possible reason is that the diluted selection pressure causes MOPSOs to fail to generate a set of good approximated Pareto solutions. In this paper, a new approach called the Hybrid Global Leader Selection Strategy (HGLSS) is proposed to deal with many-objective problems more effectively. HGLSS provides two global leader selection mechanisms: one for exploration and one for exploitation. Each particle (solution) can choose one of these two leader selection schemes to identify its global best leader. An external archive is adopted for maintaining the diversity of the found solutions and it contains the final solution reported at the end of the run. The update of the external archive is based on both Pareto dominance and density estimation. The performance of the proposed approach is compared with respect to nine state-of-the-art multi-objective metaheuristics in solving several benchmark problems. Our results indicate that the proposed algorithm generally outperforms the others in terms of Modified Inverted Generational Distance (IGD(+;)) indicator. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2020, , v. 8, p. 189527-189545 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000583548200001 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.description.validate | 202012 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Leung_Hybrid_Leader_Strategy.pdf | 7.09 MB | Adobe PDF | View/Open |
Page views
32
Last Week
0
0
Last month
Citations as of May 19, 2024
Downloads
29
Citations as of May 19, 2024
SCOPUSTM
Citations
21
Citations as of May 17, 2024
WEB OF SCIENCETM
Citations
17
Citations as of May 16, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.