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Simulated evolution of food foraging strategies of army ants
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Simulerad evolution av strategier för födosökning för armémyror (Swedish)
Abstract [en]

Many ant species make use of pheromone trails to coordinate food raids, which result in emergent behaviour in the form of complex, dynamic foraging patterns. The foraging behaviour of army ants, in particular, can be modelled as a ’central foraging problem’, where ants leave the nest to collect and bring back food. This thesis implements a previously defined model for foraging army ants to investigate what ant behavioural parameters lead to optimal solutions for three different types of food sources. These food sources are: small and common, large and scarce, and a combination of both of these food sizes and distributions. The model is also extended to investigate similar scenarios where a foraging ant colony is competing with another colony for resources. To find optimal behavioural parameters, the parameter space is searched using a simple evolutionary algorithm. This is used to successively ’evolve’ parameters to find optimal solutions. The results show that the optimal parameters, as well as foraging patterns that emerge, changed when a competing colony was present. The degree to which a solution found can be deemed ’optimal’ was highly dependent on the specific scenario. Finally, the evolution of foraging strategies for small and common was more successful than that of large and scarce. This research has applications in both natural science and computer science, where applications in the former involve, for example, swarm intelligence and optimisation algorithms.

Abstract [sv]

Många myrarter använder sig av feromoner för att leta efter mat, vilket resulterar i framväxande beteenden i form av komplexa, dynamiska mönster. För armémyror specifikt kan detta beteende modelleras som ett problem av typen ’central foraging’, där myror lämnar boet för att samla och returnera matresurser. Den här rapporten implementerar en tidigare skapad modell för resursletande armémyror för att undersöka och lokalisera beteendeparametrar till optimala lösningar för tre olika typer av matfördelningar. Dessa matfördelningar består av: små och vanliga, stora och sällsynta och en kombination av båda föregående. Modellen påbyggs även för att undersöka liknande scenarion där ytterligare en myrkoloni konkurrerar om de tillgängliga resurserna. För att hitta de optimala beteendeparametrarna används en evolutionär sökalgoritm där dessa parametrar succesivt utvecklas. Resultaten visade att de optimala parametrarna, och dess tillhörande mönsterformationer, skiljde sig mellan scenarion med en myrkoloni och scenarion med en konkurrerande myrkoloni. Hur säkert en lösning kan klassificeras som optimal varierade mellan olika scenarion, där scenarion som inkluderade små matresurser var mer framgångsrika än scenarion med stora matresurser. Denna typ av forskning har appliceringar inom både naturvetenskap och datorvetenskap, mer specifikt exempelvis svärm intelligens och optimeringsalgoritmer.

Place, publisher, year, edition, pages
2019. , p. 34
Series
TRITA-EECS-EX ; 2019:383
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259013OAI: oai:DiVA.org:kth-259013DiVA, id: diva2:1350708
Supervisors
Examiners
Available from: 2019-09-16 Created: 2019-09-12 Last updated: 2019-09-16Bibliographically approved

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