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arXiv 0909.1048cond-mat.stat-mechmath.PRq-bio.PE
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优化间歇随机游动的高效搜索

Efficient search by optimized intermittent random walks

Gleb Oshanin, Katja Lindenberg, Horacio S Wio, Sergei Burlatsky

摘要:

我们研究的动力学搜索的一个不动的目标随机移动的搜索器,检测到它只遇到。搜索器在一维网格上执行间歇性随机行走。每个节点可以以概率“alpha”踏上最近的邻居站点,或者以概率“1 - α”离开晶格以在随机方向上移动,直到它在距离出发点固定距离L处返回晶格。考虑“alpha”和L作为优化参数,通过最小化目标在最大搜索时间N内未被检测到的概率P_N来提高检测成功率。我们表明,即使在这个简单的模型中,一些非常有效的搜索策略可以导致P_N的数量级上的适当选择的“阿尔法”和L的减少。我们证明,在一般情况下,这样的最佳间歇性策略是更有效的比布朗搜索和搜索算法的基础上随机行走重尾柯西跳跃长度分布一样有效。此外,这种间歇性的策略似乎是更有利的比利维为基础的,因为它们导致更彻底的探索访问过的区域在空间中,从而使自己的并行化的搜索过程。

英文摘要:

We study the kinetics for the search of an immobile target by randomly moving searchers that detect it only upon encounter. The searchers perform intermittent random walks on a one-dimensional lattice. Each searcher can step on a nearest neighbor site with probability "alpha", or go off lattice with probability "1 - α" to move in a random direction until it lands back on the lattice at a fixed distance L away from the departure point. Considering "alpha" and L as optimization parameters, we seek to enhance the chances of successful detection by minimizing the probability P_N that the target remains undetected up to the maximal search time N. We show that even in this simple model a number of very efficient search strategies can lead to a decrease of P_N by orders of magnitude upon appropriate choices of "alpha" and L. We demonstrate that, in general, such optimal intermittent strategies are much more efficient than Brownian searches and are as efficient as search algorithms based on random walks with heavy-tailed Cauchy jump-length distributions. In addition, such intermittent strategies appear to be more advantageous than Levy-based ones in that they lead to more thorough exploration of visited regions in space and thus lend themselves to parallelization of the search processes.

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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