Dynamic multiobjective optimization algorithm based on average distance linear prediction model.
Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dyn...
| Publicado en: | Scientific World Journal pp. 389742 - 389743 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103812872&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103812872 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103812872 103812872 NLM24616625 2012505233 10.1155/2014/389742 NLM24616625 PMC3926970 103812872 ppf: 389742 ppct: 1 formats: tig: atl: Dynamic multiobjective optimization algorithm based on average distance linear prediction model. aug: au: Li, Zhiyong Chen, Hengyong Xie, Zhaoxin Chen, Chao Sallam, Ahmed affil: College of Information Science and Engineering, Hunan University, Changsha 410082, China. sug: subj: Algorithms Models, Theoretical ab: Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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