The DOI matches its Crossref record.
A New Metaheuristic Bat-Inspired Algorithm
Open accessAbstract
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Coyote optimization algorithm: A new metaheuristic for global optimization problems
Pierezan, J., & Dos Santos Coelho, L. (2018) · 2018 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477769 · Cited by 645
Bio inspired computing – A review of algorithms and scope of applications
Kar, A. K. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.018 · Cited by 578
Stock market prediction using machine learning classifiers and social media, news
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01839-w · Cited by 336
Metaheuristic Algorithms in Modeling and Optimization
Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013) · Metaheuristic Applications in Structures and Infrastructures · doi:10.1016/b978-0-12-398364-0.00001-2 · Cited by 268
Self-citationZubo, R. H. A., Mokryani, G., Rajamani, H.-S., Aghaei, J., Niknam, T., & Pillai, P. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.10.036 · Cited by 218
Metadata
- Authors
- Yang, X.-S.
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 65-74
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W2963103847
- Cited by
- 4,905
- References
- 0
- FWCI
- 84.13
- Type
- book chapter
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A New Metaheuristic Bat-Inspired Algorithm. https://app.cochat.ai/papers/10.1007/978-3-642-12538-6_6
- The paper
- Yang, X.-S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-642-12538-6_6
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.
