Emergence of an optimal search strategy from a simple random walk

Tomoko Sakiyama*, Yukio Pegio Gunji

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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
Issue number86
Publication statusPublished - 2013 Sept 6
Externally publishedYes


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

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering


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