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Cited by 4,075

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Firefly Algorithms for Multimodal Optimization

Yang, Xin-She·2009·Lecture Notes in Computer Science
DOI 10.1007/978-3-642-04944-6_14
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Abstract

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

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Metadata

Authors
Yang, X.-S.
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2009
Pages
169-178
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-642-04944-6_14
OpenAlex
W1523741643
Cited by
4,075
References
0
FWCI
28.20
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Firefly Algorithms for Multimodal Optimization. https://app.cochat.ai/papers/10.1007/978-3-642-04944-6_14
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Yang, X.-S. (2009). Firefly Algorithms for Multimodal Optimization. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-04944-6_14

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.