Bio-inspired optimization methods for visible light communication: a comprehensive review

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257630" target="_blank" >RIV/61989100:27240/25:10257630 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10462-025-11251-5" target="_blank" >https://link.springer.com/article/10.1007/s10462-025-11251-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10462-025-11251-5" target="_blank" >10.1007/s10462-025-11251-5</a>

Result language

angličtina

Original language name

Bio-inspired optimization methods for visible light communication: a comprehensive review

Original language description

Visible light communication (VLC) offers a promising alternative to traditional radio frequency communication due to its greater bandwidth, energy efficiency, and security advantages. This paper presents a comprehensive review of bio-inspired optimization algorithms, including swarm intelligence and genetic algorithms, that enhance the performance and robustness of VLC systems. These techniques have demonstrated significant potential in addressing challenges such as channel optimization and noise reduction. However, despite their advantages, bio-inspired algorithms also face limitations, including computational complexity and limited adaptability to dynamic real-world conditions. Additionally, the integration of bio-inspired methods with artificial intelligence (AI) may further enhance their adaptability and efficiency in VLC systems. This review highlights both the opportunities and challenges associated with bio-inspired optimization in VLC and provides insights into future directions for research and practical implementation, which will focus on developing more efficient and scalable bio-inspired approaches that can operate in highly variable environments while minimizing energy consumption.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

Artificial Intelligence Review

ISSN

0269-2821

e-ISSN

1573-7462

Volume of the periodical

58

Issue of the periodical within the volume

8

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

25

Pages from-to

1-25

UT code for WoS article

001488262400004

EID of the result in the Scopus database

2-s2.0-105005029462