Adaptive Lévy walks can outperform composite Brownian walks in non-destructive random searching scenarios

Adaptive Lévy walks can outperform composite Brownian walks in non-destructive random searching scenarios
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在非破坏性随机搜索场景中,自适应 Lévy 游走的性能优于复合布朗游走

DOI:
10.1016/j.physa.2008.11.007
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发表时间:
2009
期刊:
Statistical Mechanics and its Applications
影响因子:
--
通讯作者:
Reynolds A
Reynolds A
中科院分区:
--
文献类型:
--
作者:
Reynolds A

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最近,人们发现,当搜索是非破坏性的,并且当L游动对搜索中发现的条件没有反应时,复合布朗游动搜索比任何L游动更有效。本文提出了一类新的自适应Lévy游动搜索,它包含复合布朗游动作为特例。在这些新模型中,L步态搜索与更密集的布朗步态搜索交替进行。通过检测到目标,促使从广泛搜索切换到密集搜索。在这里,如果一个目标在移动了相当于“放弃距离”的距离后没有找到,就会切换回广泛的搜索。研究发现,在搜索稀疏分布资源时,自适应L步长优于复合布朗步长。因此,当资源稀少地分布在不可预测的环境中时,L进程面临更大的选择压力。这些发现与L行走搜索理论和捕食者普遍存在的两种搜索模式以及它们在发现猎物后立即切换搜索模式相一致。
Recently it has been found that composite Brownian walk searches are more efficient than any Lévy walk when searching is non-destructive and when the Lévy walks are not responsive to conditions found in the search. Here a new class of adaptive Lévy walk searches is presented that encompasses composite Brownian walks as a special case. In these new models, bouts of Lévy walk searching alternate with bouts of more intensive Brownian walk searching. Switching from extensive to intensive searching is prompted by the detection of a target. And here, switching back to extensive searching arises if a target is not located after travelling a distance equal to the ‘giving-up distance’. It is found that adaptive Lévy walks outperform composite Brownian walks when searching for sparsely distributed resources. Consequently there is stronger selection pressures for Lévy processes when resources are sparsely distributed within unpredictable environments. The findings reconcile Lévy walk search theory with the ubiquity of two modes of searching by predators and with their switching search mode immediately after finding a prey.
具有两种搜索模式的掠夺者:数学模型*
DOI: --
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期刊:
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