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arXiv 2409.04262cond-mat.softcond-mat.stat-mech
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集体趋化搜索

Collective chemotactic search

Saarland University(萨尔兰大学);University of Nottingham(诺丁汉大学)

Adam Wysocki, Hugues Meyer, Heiko Rieger

AI总结:

研究自推进剂被自身化学轨迹排斥的集体搜索,通过晶格和非晶格模型揭示弱记忆和强记忆两种机制分别通过空间分离和自回避优化搜索效率。

摘要:

我们研究了自推进剂的集体搜索,这些推进剂被自身产生的化学轨迹所排斥,这是一种同时产生间接相互作用和记忆的最小机制。使用晶格和非晶格模型,我们表明这种机制通过两种不同的机制提高了搜索效率。在弱记忆机制中,化学线索是短暂的,相互作用主要促进代理之间的空间分离。这减少了冗余探索,同时保持了移动性,从而在空间顺序和持久性之间实现了最佳权衡。在强记忆机制中,长寿命的化学轨迹诱导有效的自回避,强烈抑制重访和长搜索时间。这里最优搜索发生在有限记忆强度下:永久持续的轨迹导致自囚禁,而适度记忆能够实现高效探索。在更高密度下,重叠的化学轨迹产生集体自回避机制,在没有全局空间顺序的情况下实现显著的协同加速。总之,这些结果显示了化学介导的记忆和相互作用如何在不同的动力学机制中优化集体搜索。

英文摘要:

We investigate collective search by self-propelled agents that are repelled by their own chemically produced trails, a minimal mechanism that simultaneously generates indirect interactions and memory. Using lattice and off-lattice models, we show that this mechanism enhances search efficiency through two distinct regimes. In a weak-memory regime, chemical cues are short-lived and interactions primarily promote spatial separation between agents. This reduces redundant exploration while preserving mobility, leading to an optimal trade-off between spatial order and persistence. In a strong-memory regime, long-lived chemical trails induce effective self-avoidance, strongly suppressing revisits and long search times. Here optimal search occurs at finite memory strength: permanently persistent trails lead to self-caging, while moderate memory enables efficient exploration. At higher densities, overlapping chemical trails give rise to a collective self-avoidance mechanism that yields substantial cooperative speedup without global spatial order. Together, these results show how chemically mediated memory and interactions can optimize collective search across distinct dynamical regimes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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