Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/631AAC33-D6FE-42AB-9707-C49B1A7318F8 | - |
| Identifier | https://doi.org/10.1007/s00366-021-01554-w | - |
| Identifier | https://doi.org/10.1007/s00366-021-01554-w | - |
| Language | en | - |
| Extent | 34 pages | en |
| Title | A global optimizer inspired from the survival strategies of flying foxes | en |
| Creator | Zervoudakis Konstantinos | en |
| Creator | Ζερβουδακης Κωνσταντινος | el |
| Creator | Tsafarakis Stelios | en |
| Creator | Τσαφαρακης Στελιος | el |
| Publisher | Springer Nature | en |
| Description | This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (ΙΚΥ). | el |
| Content Summary | The aim of the current paper is to introduce a global optimization algorithm, inspired from the survival strategies of flying foxes during a heatwave, called as Flying Foxes Optimization (FFO). The proposed method exploits a Fuzzy Logic (FL) technique to determine the parameters individually for each solution, thus resulting in a parameters-free optimization algorithm. To evaluate FFO, 56 benchmark functions, including the CEC2017 test function suite and three real-world engineering problems, are employed and its performance is compared to those of state-of-the-art metaheuristics, when it comes to global optimization. The comparison results reveal that the proposed FFO optimizer constitutes a powerful attractive alternative for global optimization. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2024-02-08 | - |
| Date of Publication | 2023 | - |
| Subject | Flying Foxes Optimization | en |
| Subject | Nature-inspired algorithm | en |
| Subject | Global optimization | en |
| Subject | Self-tuning algorithm | en |
| Subject | Metaheuristic | en |
| Bibliographic Citation | K. Zervoudakis and S. Tsafarakis, “A global optimizer inspired from the survival strategies of flying foxes,” Eng. Comput., vol. 39, no. 2, pp. 1583–1616, Apr. 2023, doi: 10.1007/s00366-021-01554-w. | en |
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