A novel algorithm combining finite state method and genetic algorithm for solving crude oil scheduling problem.
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the fin...
| Publicado en: | Scientific World Journal pp. 748141 - 748142 |
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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=103820466&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103820466 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: 103820466 NLM24772031 2012565104 10.1155/2014/748141 NLM24772031 PMC3948204 103820466 ppf: 748141 ppct: 1 formats: tig: atl: A novel algorithm combining finite state method and genetic algorithm for solving crude oil scheduling problem. aug: au: Duan, Qian-Qian Yang, Gen-Ke Pan, Chang-Chun affil: Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China. sug: subj: Algorithms Artificial Intelligence Information Science Methods Petroleum Problem Solving Computer Simulation Reproducibility of Results ab: A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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