Genetic Algorithms to Solve Optimization Problems in Water Engineering

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Due to the importance of economic issues in engineering and design works, the issue of cost should be taken into consideration in the plans and implementation of construction and operation operations. In the last 50 years, the development of hardware facilities and numerical methods have made it possible to simulate the hydraulic behavior of the network even for very large networks, while the problem of determining the optimal design even for small networks remains remained. Considering that the water distribution network optimization problem is inherently a non-linear problem with a large number of discrete variables, recent research has focused on stochastic methods. Therefore, genetic algorithm was used in this research. The design of urban water transmission networks is always designed based on the minimum allowable pressure. In this research, the pressure exceeding the permissible limit is also considered as a design criterion. Although adding this parameter as a design criterion leads to a more expensive network, it leads to a longer useful life of the network. The optimization of the water distribution network using genetic algorithm (GA) technique with the design.

Autorentext

Dr. Shahide Dehghan, Ph.D., Dipartimento di Geografia, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Le competenze tecniche sono: climatologia, analisi statistica, geografia, rischi geo-naturali, geomorfologia, analisi dei fattori, analisi dei dati, cambiamenti climatici, atmosfera, riscaldamento globale, scienze del clima, investimenti assicurativi, risorse idriche.

Weitere Informationen

  • Allgemeine Informationen
    • Sprache Englisch
    • Anzahl Seiten 96
    • Herausgeber LAP LAMBERT Academic Publishing
    • Gewicht 161g
    • Untertitel DE
    • Autor Shahide Dehghan , Morteza Soltani , Hossein Gholami
    • Titel Genetic Algorithms to Solve Optimization Problems in Water Engineering
    • Veröffentlichung 20.03.2023
    • ISBN 6206151972
    • Format Kartonierter Einband
    • EAN 9786206151975
    • Jahr 2023
    • Größe H220mm x B150mm x T6mm
    • GTIN 09786206151975

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