Emergence of an optimal search strategy from a simple random walk
Emergence of an optimal search strategy from a simple random walk
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从简单的随机游走中得出最优搜索策略
DOI：
10.1098/rsif.2013.0486
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Sakiytama T and Gunji YP
中科院分区：
文献类型：
--
作者：
SAKIYAMA Tomoko;GUNJI Pegio Yukio;Sakiytama T and Gunji YP
在关于动物觅食策略的报告中，已经指出Lévy类算法代表了未知环境中的最优搜索策略，因为它们的超扩散特性和幂律分布步长。在这里，从一个简单的随机行走算法，它提供了一个随机确定的方向，在每个时间步与一个固定的移动长度，我们研究如何灵活的探索，如果一个代理改变其随机确定的下一步前进和规则，控制其随机移动的基础上，自己的方向移动的经验。我们表明，我们的算法导致一个有效的食物搜索性能相比，一个简单的随机行走算法，并表现出超扩散性能，尽管统一的步长。此外，我们的算法表现出幂律分布独立于均匀步长。
In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.