Emergence of an optimal search strategy from a simple random walk

Tomoko Sakiyama, Yukio Gunji

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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 uniformstep lengths. Moreover, our algorithmexhibited a power-law distribution independent of uniform step lengths.

Original languageEnglish
Article number0486
JournalJournal of the Royal Society Interface
Volume10
Issue number86
DOIs
Publication statusPublished - 2013 Sep 6
Externally publishedYes

Fingerprint

Animals
Food
Direction compound

Keywords

  • Optimal strategy
  • Power-law
  • Random walk
  • Super-diffusion

ASJC Scopus subject areas

  • Biophysics
  • Biotechnology
  • Bioengineering
  • Biomedical Engineering
  • Biomaterials
  • Biochemistry
  • Medicine(all)

Cite this

Emergence of an optimal search strategy from a simple random walk. / Sakiyama, Tomoko; Gunji, Yukio.

In: Journal of the Royal Society Interface, Vol. 10, No. 86, 0486, 06.09.2013.

Research output: Contribution to journalArticle

@article{d82c1cb6a7254cb0a0fa8d7d5aace39a,
title = "Emergence of an optimal search strategy from a simple random walk",
abstract = "In reports addressing animal foraging strategies, it has been stated that L{\'e}vy-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 uniformstep lengths. Moreover, our algorithmexhibited a power-law distribution independent of uniform step lengths.",
keywords = "Optimal strategy, Power-law, Random walk, Super-diffusion",
author = "Tomoko Sakiyama and Yukio Gunji",
year = "2013",
month = "9",
day = "6",
doi = "10.1098/rsif.2013.0486",
language = "English",
volume = "10",
journal = "Journal of the Royal Society Interface",
issn = "1742-5689",
publisher = "Royal Society of London",
number = "86",

}

TY - JOUR

T1 - Emergence of an optimal search strategy from a simple random walk

AU - Sakiyama, Tomoko

AU - Gunji, Yukio

PY - 2013/9/6

Y1 - 2013/9/6

N2 - 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 uniformstep lengths. Moreover, our algorithmexhibited a power-law distribution independent of uniform step lengths.

AB - 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 uniformstep lengths. Moreover, our algorithmexhibited a power-law distribution independent of uniform step lengths.

KW - Optimal strategy

KW - Power-law

KW - Random walk

KW - Super-diffusion

UR - http://www.scopus.com/inward/record.url?scp=84880843309&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880843309&partnerID=8YFLogxK

U2 - 10.1098/rsif.2013.0486

DO - 10.1098/rsif.2013.0486

M3 - Article

C2 - 23804445

AN - SCOPUS:84880843309

VL - 10

JO - Journal of the Royal Society Interface

JF - Journal of the Royal Society Interface

SN - 1742-5689

IS - 86

M1 - 0486

ER -