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Optimal Design of Water Supply Network Based on Adaptive Penalty Function and Improved Genetic Algorithm
Author(s) -
Kun Ding,
Yong Ni,
Lingfeng Fan,
Tian-Le Sun
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/8252086
Subject(s) - genetic algorithm , crossover , mathematical optimization , penalty method , convergence (economics) , optimal design , water supply network , meta optimization , computer science , cultural algorithm , water supply , population based incremental learning , function (biology) , scheme (mathematics) , algorithm , engineering , mathematics , artificial intelligence , economics , machine learning , environmental engineering , biology , economic growth , mathematical analysis , evolutionary biology
In view of the shortcomings of water supply network optimization design based on the traditional genetic algorithm in water supply safety and economy, an improved crossover operator adaptive algorithm and penalty function are proposed to improve the traditional genetic algorithm, which can effectively solve the problem of local optimal solution caused by too early convergence of the traditional genetic algorithm in pipe network optimization design. Taking a typical annular water supply network as an example, the calculation results show that the economy of the design scheme of the improved genetic algorithm is better than the traditional genetic algorithm, which fully shows that the improved genetic algorithm is practical and effective for the optimal design of water supply network.

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