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Updated: Jan 17, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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智能城市的多目标优化:对算法,挑战和未来方向的系统审查

YiFan Chen1,2, Weng Howe Chan2,3, Eileen Lee Ming Su4

  • 1Jiaxing Key Laboratory of Industrial Intelligence and Digital Twin, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang, China.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

本综述分析了智能城市的多目标优化 (MOO) 技术,发现混合人工智能和进化方法提供了卓越的适应性. 挑战仍然存在于城市规划的概括性,不确定性处理和可解释性.

关键词:
生物启发的算法生物启发的算法.计算效率 计算效率 计算效率跨领域的城市规划.机器学习增强优化优化多目标优化多目标优化智慧城市是智慧城市.可持续性权衡可以解决问题.可持续的城市发展系统的文献审查 系统的文献审查城市优化城市优化

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