Cost Optimization of a Localized Irrigation System Using Genetic Algorithms
The high cost of localized irrigation system inhibits the expansion of its application, even though it is the most efficient type of irrigation on water usage. Water is a natural, finite and chargeable resource. The population growth and the rising of population’s income require the increase of food and biomass production. The guarantee of agricultural production through irrigation with the rational use of water is a necessity and the research and development of methods to optimize the cost of the localized irrigation project can ensure the expansion of its use. This paper presents a genetic algorithm (GA-LCLI) to search a less costly localized irrigation project. The results are compared with those presented by a previous work: there is an improvement in the execution runtime and in the cost of the irrigation systems.
Keywordsevolutionary computation genetic algorithm optimization localized irrigation system
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- 1.ANA: Brazilian annual report on hydrological resources conjuncture. Technical Report (2009), http://www.ana.gov.br (accessed December 10, 2009)
- 2.Marcuzzo, F.F.N.: Sistema de otimização hidráulica e econômica de rede de irrigação localizada usando algoritmos genéticos. Thesis (Doctoral): São Carlos School of Engineering. University of São Paulo, São Carlos (2008)Google Scholar
- 3.Walski, T.M., Chase, D.V., Savic, D.A., Grayman, W., Beckwith, S., Koelle, E.: Advanced water distribution modeling and management. Haestad Press, Waterbury (2003)Google Scholar
- 7.Marcuzzo, F.F.N., Wendland, E.: Otimização de rede de irrigação de microaspersão usando algoritmos genéticos sob diferentes declividades e tarifação de Água e energia elétrica. Revista Engenharia na Agricultura. Viçosa-MG 18(1), 50–62 (2010)Google Scholar
- 8.ASAE: Standard engineering practices data: Ep 458 - field evaluation of microirrigation systems. American Society of Agricultural Engineers, pp. 792–797. ASAE, St Joseph (1996)Google Scholar
- 9.ANA: National water resources policy (2009), http://www.ana.gov.br/ingles/waterPolicy.asp (accessed December 10, 2009)
- 11.Mitchell, M.: An introduction to genetic algorithms. MIT Press, Cambridge (1996)Google Scholar
- 12.Kumar, R.: System and method for the use of an adaptative mutation operator in genetic algorithms. United States Patent (February 2010) Patent No US 7,660,773 B1Google Scholar