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Cited by 1,630

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Flower Pollination Algorithm for Global Optimization

Open access
Yang, Xin-She·2012·Lecture Notes in Computer Science
DOI 10.1007/978-3-642-32894-7_27
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Abstract

Flower pollination is an intriguing process in the natural world. Its evolutionary characteristics can be used to design new optimization algorithms. In this paper, we propose a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers. We first use ten test functions to validate the new algorithm, and compare its performance with genetic algorithms and particle swarm optimization. Our simulation results show the flower algorithm is more efficient than both GA and PSO. We also use the flower algorithm to solve a nonlinear design benchmark, which shows the convergence rate is almost exponential.

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Crossref · 10.1007/978-3-642-32894-7_27

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Metadata

Authors
Yang, X.-S.
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2012
Pages
240-249
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-642-32894-7_27
OpenAlex
W1606402766
Cited by
1,630
References
12
FWCI
14.77
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Flower Pollination Algorithm for Global Optimization. https://app.cochat.ai/papers/10.1007/978-3-642-32894-7_27
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Yang, X.-S. (2012). Flower Pollination Algorithm for Global Optimization. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-32894-7_27

Data sources

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.