Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem
This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
1999
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/14615/1/14615_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14615/2/14615_2.doc |
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| _version_ | 1864513384162000896 |
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| author | Mantawy, A.H. |
| author2 | Abdel-Magid, Y.L. Selim, S.Z. unknown |
| author2_role | author author author |
| author_facet | Mantawy, A.H. Abdel-Magid, Y.L. Selim, S.Z. unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Mantawy, A.H. Abdel-Magid, Y.L. Selim, S.Z. unknown |
| dc.date.none.fl_str_mv | 1999-08 2020 |
| dc.format.none.fl_str_mv | application/pdf application/msword |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14615/1/14615_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14615/2/14615_2.doc (1999) Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem. Power Systems, IEEE Transactions on, 14. |
| dc.language.none.fl_str_mv | en en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14615/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Computer |
| dc.title.none.fl_str_mv | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithms |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_c3126e9fa9f488c7d06106ec4dcd8d9b |
| identifier_str_mv | (1999) Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem. Power Systems, IEEE Transactions on, 14. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::14615 |
| publishDate | 1999 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problemMantawy, A.H.Abdel-Magid, Y.L.Selim, S.Z.unknownComputerThis paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithmsIEEE1999-082020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/mswordhttps://eprints.kfupm.edu.sa/id/eprint/14615/1/14615_1.pdfhttps://eprints.kfupm.edu.sa/id/eprint/14615/2/14615_2.doc (1999) Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem. Power Systems, IEEE Transactions on, 14. enenhttps://eprints.kfupm.edu.sa/id/eprint/14615/info:eu-repo/semantics/openAccessoai::146152019-11-01T14:06:38Z |
| spellingShingle | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem Mantawy, A.H. Computer |
| status_str | publishedVersion |
| title | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| title_full | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| title_fullStr | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| title_full_unstemmed | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| title_short | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| title_sort | Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem |
| topic | Computer |
| url | https://eprints.kfupm.edu.sa/id/eprint/14615/1/14615_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14615/2/14615_2.doc |