Skip to main content

2014 | OriginalPaper | Buchkapitel

11. Fruit Fly Optimization Algorithm

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this chapter, we present a novel optimization algorithm called fruit fly optimization algorithm (FFOA) which is inspired by the behaviour of fruit flies. We first describe the general knowledge of the foraging behaviour of fruit flies in Sect. 11.1. Then, the fundamentals and performance of FFOA are introduced in Sect. 11.2. Finally, Sect. 11.3 summarises this chapter.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Abidin, Z. Z., Hamzah, M. S. M., Arshad, M. R., & Ngah, U. K. (2012). A calibration framework for swarming ASVs’ system design. Indian Journal of Geo-Marine Sciences, 41, 581–588. Abidin, Z. Z., Hamzah, M. S. M., Arshad, M. R., & Ngah, U. K. (2012). A calibration framework for swarming ASVs’ system design. Indian Journal of Geo-Marine Sciences, 41, 581–588.
Zurück zum Zitat Chapman, R. F. (2013). In S. J. Simpson, A. E. Douglas (Eds.) The insects: structure and function. New York: Cambridge University Press. ISBN 978-0-521-11389-2. Chapman, R. F. (2013). In S. J. Simpson, A. E. Douglas (Eds.) The insects: structure and function. New York: Cambridge University Press. ISBN 978-0-521-11389-2.
Zurück zum Zitat Chen, P.-W., Lin, W.-Y., Huang, T.-H., & Pan, W.-T. (2013). Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service. Applied Mathematics and Information Sciences, 7, 459–465.CrossRef Chen, P.-W., Lin, W.-Y., Huang, T.-H., & Pan, W.-T. (2013). Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service. Applied Mathematics and Information Sciences, 7, 459–465.CrossRef
Zurück zum Zitat Han, J.-Y., & Liu, C.-Z. (2013). Fruit fly optimization algorithm with adaptive mutation (in Chinese). Application Research of Computers, 30, 1–6. (in Chinese). Han, J.-Y., & Liu, C.-Z. (2013). Fruit fly optimization algorithm with adaptive mutation (in Chinese). Application Research of Computers, 30, 1–6. (in Chinese).
Zurück zum Zitat Li, H., Guo, S., Zhao, H., Su, C., & Wang, B. (2012). Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Energies, 5, 4430–4445.CrossRef Li, H., Guo, S., Zhao, H., Su, C., & Wang, B. (2012). Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Energies, 5, 4430–4445.CrossRef
Zurück zum Zitat Li, H.-Z., Guo, S., Li, C.-J., & Sun, J.-Q. (2013). A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems, 37, 378–387.CrossRef Li, H.-Z., Guo, S., Li, C.-J., & Sun, J.-Q. (2013). A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems, 37, 378–387.CrossRef
Zurück zum Zitat Liu, Y., Wang, X. & Li, Y. (2012, July 6–8). A modified fruit-fly optimization algorithm aided PID controller designing. In IEEE 10th World Congress on Intelligent Control and Automation. (pp. 233–238). Beijing, China. Liu, Y., Wang, X. & Li, Y. (2012, July 6–8). A modified fruit-fly optimization algorithm aided PID controller designing. In IEEE 10th World Congress on Intelligent Control and Automation. (pp. 233–238). Beijing, China.
Zurück zum Zitat Pan, W.-T. (2011). Fruit fly optimization algorithm . Taiwan: Tsang Hai Book Publishing Co. ISBN 978-986-6184-70-3. (in Chinese). Pan, W.-T. (2011). Fruit fly optimization algorithm . Taiwan: Tsang Hai Book Publishing Co. ISBN 978-986-6184-70-3. (in Chinese).
Zurück zum Zitat Pan, W.-T. (2012). A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.CrossRef Pan, W.-T. (2012). A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.CrossRef
Zurück zum Zitat Shimada, T., Kato, K., Kamikouchi, A., & Ito, K. (2005). Analysis of the distribution of the brain cells of the fruit fly by an automatic cell counting algorithm. Physica A, 350, 144–149.CrossRef Shimada, T., Kato, K., Kamikouchi, A., & Ito, K. (2005). Analysis of the distribution of the brain cells of the fruit fly by an automatic cell counting algorithm. Physica A, 350, 144–149.CrossRef
Zurück zum Zitat Touhara, K. (Ed.) (2013). Pheromone signaling: Methods and protocols. London: Springer. ISBN 978-1-62703-618-4. Touhara, K. (Ed.) (2013). Pheromone signaling: Methods and protocols. London: Springer. ISBN 978-1-62703-618-4.
Zurück zum Zitat Tu, C.-S., Chang, C.-T., Chen, K–. K., & Lu, H.-A. (2012). A study on business performance with the combination of Z-score and FOAGRNN hybrid model. African Journal of Business Management, 6, 7788–7798. Tu, C.-S., Chang, C.-T., Chen, K–. K., & Lu, H.-A. (2012). A study on business performance with the combination of Z-score and FOAGRNN hybrid model. African Journal of Business Management, 6, 7788–7798.
Zurück zum Zitat Wang, L., Zheng, X.-L., & Wang, S.-Y. (2013). A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based Systems, 48, 17–23.CrossRef Wang, L., Zheng, X.-L., & Wang, S.-Y. (2013). A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based Systems, 48, 17–23.CrossRef
Zurück zum Zitat Zhu, W., Li, N., Shi, C., & Chen, B. (2013). SVR based on FOA and its application in traffic flow prediction. Open Journal of Transportation Technologies, 2, 6–9. (in Chinese).CrossRef Zhu, W., Li, N., Shi, C., & Chen, B. (2013). SVR based on FOA and its application in traffic flow prediction. Open Journal of Transportation Technologies, 2, 6–9. (in Chinese).CrossRef
Metadaten
Titel
Fruit Fly Optimization Algorithm
verfasst von
Bo Xing
Wen-Jing Gao
Copyright-Jahr
2014
DOI
https://doi.org/10.1007/978-3-319-03404-1_11