The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86097037" target="_blank" >RIV/61989100:27240/14:86097037 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.3233/IFS-131020" target="_blank" >http://dx.doi.org/10.3233/IFS-131020</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/IFS-131020" target="_blank" >10.3233/IFS-131020</a>

Result language
angličtina
Original language name
The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization
Original language description
In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOPSO-FACO). This hybridization solves the multi-objective problem, which relies on both time performance criteriaand the shortest path. Experimental results illustrate that the proposed method is efficient. (C) 2014 - IOS Press and the authors. All rights reserved.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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