优化持久随机搜索
Optimizing persistent random searches
摘要：
本文考虑具有短程记忆的持久随机序列的极小模型。我们精确地计算这样的平均首次通过时间的目标在一个有界域，并发现它承认一个非平凡的最小值作为持久长度的函数。这揭示了一个最佳的搜索策略，显着不同于简单的弹道运动的情况下，泊松分布的目标。我们的研究结果表明，目标的分布在随机搜索问题中起着至关重要的作用。特别是，在生物相关的情况下，无论是一个单一的目标或定期模式的目标，我们发现，在强烈的对比与文献中的重复声明，持续随机游走指数分布的游览长度可以最大限度地减少搜索时间，并在这个意义上表现得比任何利维行走。
英文摘要：
We consider a minimal model of persistent random searcher with short range memory. We calculate exactly for such searcher the mean first-passage time to a target in a bounded domain and find that it admits a non trivial minimum as function of the persistence length. This reveals an optimal search strategy which differs markedly from the simple ballistic motion obtained in the case of Poisson distributed targets. Our results show that the distribution of targets plays a crucial role in the random search problem. In particular, in the biologically relevant cases of either a single target or regular patterns of targets, we find that, in strong contrast with repeated statements in the literature, persistent random walks with exponential distribution of excursion lengths can minimize the search time, and in that sense perform better than any Levy walk.
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。
