Towards an Adaptive Levy Walk using Artificial Endocrine Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Behavioural adaptation is often observed in foraging animals via coupling periods of localized search and long straightforward motions, a well-known strategy named Lévy Walk. In this paper we propose an adaptive Lévy Walk model to control an autonomous agent. The model is comprised of a Lévy-based controller modulated by an artificial endocrine system optimised through evolutionary techniques. This new approach enables the agent to control the transition between localized search and long relocation when exposed to external stimulus. The model is tested in exploration tasks where environments have resources clustered into patches. Further tests incorporated environments with different patch characteristics, such as patch size or resource distribution within patches. Our model has shown to outperform the benchmark approach in terms of search efficiency, highlighting the benefits of combining a Lévy Walk based controller with a biologically inspired strategy for adaptation.
Original languageEnglish
Title of host publicationADAPTIVE 2020 : The Twelfth International Conference on Adaptive and Self-Adaptive Systems and Applications
PublisherIARIA
Pages116-121
Number of pages6
ISBN (Print)9781612087818
Publication statusPublished - Oct 2020
Event12th International Conference on Adaptive and Self-Adaptive Systems and Applications 2020 - France, Nice, France
Duration: 25 Oct 202029 Oct 2020
https://www.iaria.org/conferences2020/ADAPTIVE20.html

Publication series

NameADAPTIVE, The Twelfth International Conference on Adaptive and Self-Adaptive Systems and Applications
ISSN (Print)2308-4146

Conference

Conference12th International Conference on Adaptive and Self-Adaptive Systems and Applications 2020
Abbreviated titleADAPTIVE 2020
CountryFrance
CityNice
Period25/10/2029/10/20
Internet address

Keywords

  • Artificial Endocrine Systems
  • Adaptation
  • Lévy Walk
  • Biologically Inspired Algorithms
  • Foraging
  • Autonomous Agents

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