Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014630" target="_blank" >RIV/62690094:18450/18:50014630 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417418304160" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417418304160</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2018.07.001" target="_blank" >10.1016/j.eswa.2018.07.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Popis výsledku v původním jazyce

Large-scale models are currently used for the simulation, analysis and control of real systems, whether technical, biological, social or economic. In multi-agent simulations of virtual economies, it is important to schedule a large number of agents across the cities involved, in order to establish a functional supply chain network for industrial production. This study describes an experimental evaluation of path planning approaches in the field of multi-agent modelling and simulation, applied to a large-scale setting. The experimental comparison is based on a model in which agents represent economic entities and can participate in mutual interactions. For the purposes of experiment, the model is scaled to various degrees of complexity in terms of the numbers of agents and transportation nodes. Various numbers of agents are used to explore the way in which the model&apos;s complexity influences the runtime of the path-planning task. The results indicate that there are significant differences between the runtime performances associated with single approaches, for differing levels of system complexity and model sizes. The study reveals that the appropriate sharing of shortest path information can significantly improve path-planning activities. Hence, this work extends current research in the field of path-planning for multi-agent simulations by conducting an experimental performance analysis of five distinct path-planning approaches and a statistical evaluation of the results. This statistical evaluation contrasts with performance analyses conducted on the basis of &apos;Big O&apos; notation for algorithmic complexity, which describes the limiting behaviour of the algorithm and gives only a rough performance estimate.

Název v anglickém jazyce

Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Popis výsledku anglicky

Large-scale models are currently used for the simulation, analysis and control of real systems, whether technical, biological, social or economic. In multi-agent simulations of virtual economies, it is important to schedule a large number of agents across the cities involved, in order to establish a functional supply chain network for industrial production. This study describes an experimental evaluation of path planning approaches in the field of multi-agent modelling and simulation, applied to a large-scale setting. The experimental comparison is based on a model in which agents represent economic entities and can participate in mutual interactions. For the purposes of experiment, the model is scaled to various degrees of complexity in terms of the numbers of agents and transportation nodes. Various numbers of agents are used to explore the way in which the model&apos;s complexity influences the runtime of the path-planning task. The results indicate that there are significant differences between the runtime performances associated with single approaches, for differing levels of system complexity and model sizes. The study reveals that the appropriate sharing of shortest path information can significantly improve path-planning activities. Hence, this work extends current research in the field of path-planning for multi-agent simulations by conducting an experimental performance analysis of five distinct path-planning approaches and a statistical evaluation of the results. This statistical evaluation contrasts with performance analyses conducted on the basis of &apos;Big O&apos; notation for algorithmic complexity, which describes the limiting behaviour of the algorithm and gives only a rough performance estimate.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert systems with applications

ISSN

0957-4174

e-ISSN

—

Svazek periodika

113

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

415-427

Kód UT WoS článku

000446288600027

EID výsledku v databázi Scopus

2-s2.0-85049933474