Multireservoir system optimization using multi-objective genetic algorithms

Taesoon Kim, Jun-Haeng Heo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

The application of multi-objective genetic algorithms (MOGA) to optimize system of the Han River basin in South Korea was discussed. The solutions of the MOGA yielded a trade-off curve or surface, identifying a population of points that define optimal solutions to the problem. It was found that non-dominating sorting approach was used to get the non-dominated fronts and maintaining a diverse set of solutions in the non-dominated fronts was achieved by sharing. The results show that MOGA has been an effective solution technique for solving multireservoir system optimization.

Original languageEnglish
Title of host publicationProceedings of the 2004 World Water and Environmetal Resources Congress
Subtitle of host publicationCritical Transitions in Water and Environmental Resources Management
EditorsG. Sehlke, D.F. Hayes, D.K. Stevens
Pages1900-1909
Number of pages10
Publication statusPublished - 2004 Dec 1
Event2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management - Salt Lake City, UT, United States
Duration: 2004 Jun 272004 Jul 1

Publication series

NameProceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management

Other

Other2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management
CountryUnited States
CitySalt Lake City, UT
Period04/6/2704/7/1

Fingerprint

Genetic algorithms
Sorting
Catchments
Rivers

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Kim, T., & Heo, J-H. (2004). Multireservoir system optimization using multi-objective genetic algorithms. In G. Sehlke, D. F. Hayes, & D. K. Stevens (Eds.), Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmental Resources Management (pp. 1900-1909). (Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management).
Kim, Taesoon ; Heo, Jun-Haeng. / Multireservoir system optimization using multi-objective genetic algorithms. Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmental Resources Management. editor / G. Sehlke ; D.F. Hayes ; D.K. Stevens. 2004. pp. 1900-1909 (Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management).
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Kim, T & Heo, J-H 2004, Multireservoir system optimization using multi-objective genetic algorithms. in G Sehlke, DF Hayes & DK Stevens (eds), Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmental Resources Management. Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management, pp. 1900-1909, 2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management, Salt Lake City, UT, United States, 04/6/27.

Multireservoir system optimization using multi-objective genetic algorithms. / Kim, Taesoon; Heo, Jun-Haeng.

Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmental Resources Management. ed. / G. Sehlke; D.F. Hayes; D.K. Stevens. 2004. p. 1900-1909 (Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The application of multi-objective genetic algorithms (MOGA) to optimize system of the Han River basin in South Korea was discussed. The solutions of the MOGA yielded a trade-off curve or surface, identifying a population of points that define optimal solutions to the problem. It was found that non-dominating sorting approach was used to get the non-dominated fronts and maintaining a diverse set of solutions in the non-dominated fronts was achieved by sharing. The results show that MOGA has been an effective solution technique for solving multireservoir system optimization.

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Kim T, Heo J-H. Multireservoir system optimization using multi-objective genetic algorithms. In Sehlke G, Hayes DF, Stevens DK, editors, Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmental Resources Management. 2004. p. 1900-1909. (Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management).