来自:一种以鱼类识别为灵感的优化方法,用于流动环境中的多代理决策问题

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Yuchen Wang¹, Lei Sun¹

¹School of Software, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

Biomimetics (Basel, Switzerland)

|April 25, 2025

概括

一种新的鱼类识别灵感优化方法 (FROM) 通过改进邻居拓和学习策略来增强水下多代理系统. 这种方法有效地解决了动态流体环境中的水力动力学约束.

科学领域:

机器人和控制系统机器人和控制系统
流体动力学流体动力学
人工智能的人工智能

背景情况:

水下多代理系统 (UMAS) 受到水力动力学约束的阻碍,降低了传统优化算法的性能.
动态流体环境给UMAS的决策和协调带来了重大挑战.
现有的优化方法很难适应水下环境中复杂的水力动力学相互作用.

研究的目的:

为在流体环境中运行的UMAS引入一种灵感来自鱼类识别的新型优化方法.
为了解决UMAS中水力动力学约束引起的性能退化.
增强UMAS在动态水下条件下的决策能力.

主要方法:

开发了一种以鱼类识别为灵感的优化方法 (FROM),结合了鱼类识别特征.
实施了基于视觉识别的邻居拓改进策略.
引入了基于水力动力学识别的学习策略改进.
分析了 FROM 算法的计算复杂性.

主要成果:

与现有方法相比,FROM算法表现出优越的性能.
统计分析显示,最小值,最大值,标准偏差,平均值和中位数的目标函数值显著改善.
发现FROM的计算复杂性对于实际应用是可以接受的.

结论:

拟议的FROM算法为在流动环境约束下多个代理决策问题提供了强大的解决方案.
在动态水力动力学条件下,FROM有效地克服了传统算法的局限性.
这种以鱼类识别为灵感的方法为UMAS未来的研究提供了有希望的方向.

关键词:

生物灵感优化方法的优化方法.鱼类识别功能鱼类识别功能水力动力学分析多代理学习多代理学习

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