SavedAdd your journal

Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 3 Sayı: 3

The use of genetic algorithm and particle swarm algorithm in determining egg freshness

Hasan Alp Sahin(ONDOKUZ MAYIS ÜNİVERSİTESİ)0000-0002-7811-955X
Hasan Onder(ONDOKUZ MAYIS ÜNİVERSİTESİ)0000-0002-8404-8700
Pages
81–88
Publication Date
2020-07-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The coefficient of determination obtained were quite low and it was understood that these two methods could not be used to determine the freshness of eggs.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it is aimed to determine the genetic algorithm optimization (GAO) and particle swarm algorithm optimization (PSO) activities in the artificial intelligence applications group.
📊
Key Findings & Data:As experimental material, 50 eggs were photographed for 29 days and the images were used as data.
💡
Conclusion & Impact:The coefficient of determination obtained were quite low and it was understood that these two methods could not be used to determine the freshness of eggs.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Genetic algorithm · Particle swarm algorithm · Egg

Abstract

In this study, it is aimed to determine the genetic algorithm optimization (GAO) and particle swarm algorithm optimization (PSO) activities in the artificial intelligence applications group. As experimental material, 50 eggs were photographed for 29 days and the images were used as data. According to the findings, the coefficient of determination obtained from the PSO classification was 0.07 and the coefficient of determination obtained from the GAO classification was 0.14. The results obtained from the GAO and PSO algorithms used to determine the freshness of the egg show that both methods are insufficient for the specified purpose. The coefficient of determination obtained were quite low and it was understood that these two methods could not be used to determine the freshness of eggs.

🔖

Cite This Article

Sahin, H. A., Onder, H. (2020). The use of genetic algorithm and particle swarm algorithm in determining egg freshness. Black Sea Journal of Engineering and Science, Cilt: 3 Sayı: 3, 81–88. https://doi.org/10.34248/bsengineering.684613

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (31)

  1. Abdel-Nour N, Ngadi M, Prasher S, Karimi Y. 2011. Prediction of egg freshness and albumen quality using visible/near ınfrared spectroscopy. Foof Bioprocess Technol, 4: 731-736.
  2. Aboonajmi M, Setarehdan SK, Akram A, Nishizu T, Kondo N. 2014. Prediction of poultry egg freshness using ultrasound. International Journal of Food Properties, 17(9): 1889-1899.
  3. Aboonajmi M, Najafabadi TA. 2014. Prediction of poultry egg freshness using vis-nir spectroscopy with maximum likelihood method. International Journal of Food Properties, 17(10): 2166-2176.
  4. Arumugam MS, Chandramohan A. 2007. A New and ımproved version of particle swarm optimization algorithm with global-local best parameters. Knowl Inf Syst. DOI: 10.1007/s10115-007-0109-z.
  5. Baş N. 2006. Yapay sinir ağları yaklaşımı ve bir uygulama. Mimar Sinan Güzel Sanatlar Üniversitesi. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
  6. Bolat B. 2006. Asansör kontrol sistemlerinin genetik algoritma ile simülasyonu. Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, İstanbul.
  7. Chatterjee S, Laudato M. 1995. Gender and performance in athletics. Social Biology, 42: 397-412.
  8. Chen X, Xun Y, Li W, Zhang J. 2010. Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture, 71: 48-53.
  9. Çanakçı M, Hosoz M. 2006. Energy and exergy analyses of a diesel engine fuelled with various biodiesels. Energy Sources, Part B: 379–394.
  10. Dede T. 2003. Değer kodlaması kullanarak kafes sistemlerin genetik algoritma ile minimum ağırlıklı boyutlandırılması. Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Trabzon.
  11. Demirbaş HY, Dursun İ. 2007. Buğday tanelerinin bazı fiziksel özelliklerinin görüntü işleme tekniğiyle belirlenmesi. Ankara Üniversitesi, Tarım Bilimleri Dergisi, 13(3): 176-185.
  12. Eldem H. 2014. Karınca Koloni Optimizasyonu (KKO) ve Parçacık Sürü Optimizasyonu (PSO) Algortimaları temelli bir hiyerarşik yaklaşım geliştirilmesi. Selçuk Üniversitesi, FenBilimleri Enstitüsü, Yüksek Lisans Tezi, Konya.
  13. Elmas Ç. 2003. Fuzzy logic controllers. 1st ed, Seçkin Press, Ankara. pp. 35-40.
  14. Fan SKS, Chiu YY. 2007. A decreasing ınertia weight particle swarm optimizer. Engineering Optimization, 39(2): 203 – 228.
  15. Haugh RR. 1937. The haugh unit for measuring egg quality. US Egg Poultry Magazine, 43: 552–555.
  16. Hemasian-Etefagh F, Safi-Esfahani F. 2019. Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing. The Journal of Supercomputing, 75(10): 6386–6450. DOI: 10.1007/s11227-019-02832-7.
  17. Karoui R, Kemps B, Bamelis F, De Katelaere B, Decuypere E, De Baerdemaeker J. 2006. Methods to evaluate egg freshness in research and industry: A review. European Food Research Technology, 222: 727–732.
  18. Karoui R, Nicolaï B, De Baerdemaeker J. 2008. Monitoring the egg freshness during storage under modified atmosphere by fluorescence spectroscopy. Food and Bioprocess Technology, 1: 346–356.
  19. Kennedy J, Eberhart R. 1995. Particle swarm optimization, IEEE International conference on neural networks. Perth, Australia. IEEE Servive Center, Piscataway, NJ, 1942-1948.
  20. Koç ML. 2002. Taş dolgu dalgakıranların yapay sinir ağları, bulanık mantık sistemleri ve genetik algoritma ile ön tasarımı ve güvenirlik analizi. Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Ankara.
  21. Meng XH, Lin YF, Qui D. 2017. Hybrid algorithm of adaptive inertia weight particle swarm and simulated annealing. International Journal of Computer Techniques, 4(2): 105-110.
  22. Özsağlam MY. 2009. Parçacık Sürü Optimizasyonu algoritmasının gezgin satıcı problemine uygulanması ve performansının incelenmesi. Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Konya.
  23. Öztürk HT. 2013. Deprem bölgelerinde yapılacak betonarme sığ tünellerin yapay arı koloni algoritması ve genetik algoritmayla optimum tasarımı. Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Trabzon.
  24. Parlak M. 2007. Genetik algoritmaların hesapsal ve yapısal olarak incelenmesi. Ondokuz Mayıs Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Samsun.
  25. Ratnaweera A, Halgamuge SK, Watson C. 2004. Self-Organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient. IEEE Trans Evol Comput, 8(3): 240–255.
  26. Robinson DS, Monsey JB. 1972. Changes in the composition of ovomucin during liquefaction of thick white. Journal of the Science of Food and Agriculture, 23: 29–38.
  27. Shi Y, Eberhart RC. 1998. A modified particle swarm optimizer. In The IEEE International Conference of Evolutionary Computation, 69–73, Anchorage, Alaska
  28. Syahputra MF, Felicia V, Rahmat RF, Budiarto R. 2016. Scheduling diet for diabetes mellitus patients using genetic algorithm. Journal of Physics, 801: 012033. DOI: 10.1088/1742-6596/801/1/012033.
  29. Tamer S, Karakuzu C. 2006. Parçacık sürüsü optimizasyon algoritmasın ve benzetim örnekleri. (Eleco'06) Elektrik-Elektronik-Bilgisayar Mühendisliği Sempozyumu ve Fuarı Bildirileri.
  30. Wells PC, Norris KH. 1987. Egg quality—current problem and recent advances. In B. M. Freeman (Ed.), Egg quality— current problems and recent advances. Abingdon: Carfax.
  31. Zhang JR, Zhang J, Lok TM, Lyu MR. 2007. A hybrid particle swarm optimizationback-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 185: 1026-1037.