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IA-DTPSO:一个多策略的集成粒子群集优化,用于预测中国的城市总水资源

Zheyu Zhu¹, Jiawei Wang^1,2, Kanhua Yu^1,3

¹School of Architecture, Chang'an University, Xi'an 710061, China.

Biomimetics (Basel, Switzerland)

|April 25, 2025

概括

本研究介绍了IA-DTPSO,一种增强的粒子群优化,以提高搜索效率并避免局部最佳. IA-DTPSO在复杂的优化任务中表现出卓越的性能,并准确地预测城市水资源.

科学领域:

计算智能是一种计算智能.
优化算法优化算法
数据科学数据科学数据科学

背景情况:

粒子集群优化 (PSO) 受到低搜索效率和局部优化的影响.
现有的优化方法需要对复杂,高维度的问题进行增强.

研究的目的:

提出一个新的多策略粒子群集优化 (PSO) 与信息获取,命名为IA-DTPSO.
增强PSO算法的勘探和开发能力.
应用改进的算法来优化时间依赖的灰色模型和预测城市水资源.

主要方法:

使用Sobol序列初始化来实现均的粒子分布.
开发了基于信息获取的更新方案,并采用了适应性信息处理.
包含了针对定位更新和触点飞行策略的斯皮尔曼相关系数 (SCC).
引入了一个维度学习策略,以增强人口多样性.

主要成果:

在CEC2022基准上,IA-DTPSO展示了卓越的勘探和开发 (ENE) 能力.
在10个 (58.33%) 和20个 (41.67%) 维度中,在CEC2022功能上取得了显著的成功率.
优化了时间依赖灰色模型 (TDGM) 的参数,在预测中国城市总水资源 (TUWR) 时实现了最低的平均绝对百分比误差 (MAPE) 5.9439%.

结论:

IA-DTPSO有效地克服了传统的公共服务任务的局限性.
该算法在一般优化基准和专门的预测任务中表现强.
IA-DTPSO提供了一种强大而准确的方法来预测城市水等关键资源,其性能优于现有模型.

关键词:

索波尔序列初始化初始化斯皮尔曼的相关系数维度学习战略维度学习战略信息获取战略信息获取战略粒子群集优化粒子群集优化预测中国城市总水资源的预测.触点飞行战略触点飞行战略三个参数灰色组合优化模型的模型.

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