LLM-Enhanced metaheuristics with single-shot and few-shot learning for multi-robot exploration tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260595" target="_blank" >RIV/61989100:27240/25:10260595 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10586-025-05585-6?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=clarivate" target="_blank" >https://link.springer.com/article/10.1007/s10586-025-05585-6?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10586-025-05585-6" target="_blank" >10.1007/s10586-025-05585-6</a>
Alternative languages
Result language
angličtina
Original language name
LLM-Enhanced metaheuristics with single-shot and few-shot learning for multi-robot exploration tasks
Original language description
Multi-robot exploration in unknown environments is a challenging optimization task. Although many optimization algorithms exist, there has been limited exploration of Large Language Models (LLMs) in the literature to enhance these methods. This study investigates the use of LLMs to improve optimization strategies for robotic exploration considering two main approaches: zero-shot and few-shot learnings. In the former case, the LLM generates solutions based on a problem description alone. In the latter, few-shot learning, the LLM refines solutions based on initial results as well as a problem description. Using models like GPT, Gemini, and Claude, we created enhanced variants of Particle Swarm Optimization (PSO) to improve exploration efficiency and help robots navigate complex environments to address PSO’s common issues such as local optima. Experimental results demonstrate the strong potential of LLMs in refining optimization strategies for multi-robot exploration. © The Author(s) 2025.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cluster Computing-The Journal of Networks Software Tools and Applications
ISSN
1386-7857
e-ISSN
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Volume of the periodical
28
Issue of the periodical within the volume
16
Country of publishing house
US - UNITED STATES
Number of pages
35
Pages from-to
1-35
UT code for WoS article
001597096900032
EID of the result in the Scopus database
2-s2.0-105019198118