Investigation of The Effect of Feeding Period in Honey Bee Algorithm

Yıl 2022, Cilt: 26 Sayı: 6, 1071 - 1083, 31.12.2022

Mustafa Kaya

https://doi.org/10.16984/saufenbilder.1031673

Öz

In the study, it was investigated the ejaculation ability and semen quality of drones, according to feeding with pollen in different periods. In the first step of the study, 16 %, 32 %, 47 %, 63 %, 79 %, and 100 % feeding periods were applied to the drones, for investigating the effect on ejaculation ability, and the semen quality of drones was investigated. While investigating these feeding period effects “0-1”, bonded, and unbounded knapsack optimization problems were used. After the most effective feeding period was determined, this period was applied to the traveling salesman and liquid storage tank problems in the second step of the study. In the analysis of the traveling salesman problem, it was determined the shortest way between two cities. Analysis of the liquid storage tank problem, it was determined the minimum connector areas. As a result, the analysis results showed that the performance of the artificial bee colony algorithm is very good while solving too complex engineering optimization problems.

Anahtar Kelimeler

Bee colony algorithm, feding period effect, knapsack problems

Kaynakça

• [1] D. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, “The Bees Algorithm A Novel Tool For Complex Optimization Problems”, Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (EPROMS 2006). Elsevier Science Ltd, Cardiff, UK, 2006.
• [2] D. Pham, A. Ghanbarzadeh, “Multiobjective Optimization Using The Bees Algorithm”, 3rd International Virtual Conference on Intelligent Production Machines and Systems (EPROMS 2007), Whittles, Dunbeath, Scotland, pp. 111-116, 2007.
• [3] B. Yuce, D. Pham D, M. Packianather, E. Mastrocinque, “An Enhancement To The Bees Algorithm With Slope Angle Computation And Hill Climbing Algorithm And Its Applications In Scheduling And Continuous Type Optimization Problem”, Production & Manufacturing Research, vol. 3, pp. 3-19, 2015.
• [4] S. Abdullah, M. Alzaqebah, “A hybrid Self Adaptive Bees Algorithm For Examination Timetabling Problems”, Applied Soft Computing, vol. 13, pp. 3608-3620, 2013.
• [5] C. Lara, JJ. Flores, F. Calderón, “Solving A School Timetabling Problem Using A Bee Algorithm”, Advances in Artificial Intelligence. Springer, Berlin, Heidelberg, 2008.
• [6] Z. Dongli, G. Xinping, T. Yinggan, T. Yong, “Modified Artificial Bee Colony Algorithms for Numerical Optimization”, in Proc. 3rd International Workshop on Intelligent Systems and Applications, 2011, pp. 1–4, 2011.
• [7] A. Banharnsakun, T. Achalakul and B. Sirinaovakul B, “The best-so-far selection in Artificial Bee Colony algorithm”, Applied Soft Computing, Vol. 11, no. 2, pp. 2888-2901, 2011.
• [8] S. Anusara, A. Selamatab R. Sallehuddin, “A modified scout bee for artificial bee colony algorithm and its performance on optimization problems”, Journal of King Saud University - Computer and Information Sciences, vol 28, no. 4, pp. Pages 395-406, 2016.
• [9] I. Brajevic, M. Tuba, “An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems”, manufacturing, vol. 24, pp. 729–740, 2013.
• [10] K. Diwold, A. Andrej, A. Scheidler, “Middendorf M, Performance evaluation of artificial bee colony optimization and new selection schemes”, Memetic Computing, vol. 3, pp. 149-162, 2011.
• [11] E. Cuevas, F. Sención, D. Zaldivar, M. Pérez-Cisneros and H. Sossa, “A multi-threshold segmentation approach based on Artificial Bee Colony optimization”, Applied Intelligence, vol. 37, pp. 321–336, 2012.
• [12] PW. Tsai, JS. Pan, BY. Liao, SC. Chu (2009), “Enhanced Artificial Bee Colony Optimization”, International Journal of Innovative Computing. Information and Control, vol. 5, pp. 5081–5092, 2009.
• [13] B. Alatas, “Chaotic Bee Colony Algorithms for Global Numerical Optimization”, Expert System Application, vol. 37 (2010) pp. 5682–5687, 2010.
• [14] F. Kang, J. Lie, Z. Ma, “Rosenbrock Artificial Bee Colony Algorithm for Accurate Global Optimization of Numerical Functions”, Information Sciences vol. 181, pp. 3508–3531, 2011.
• [15] D. Karaboga, B. Akay, “A Modified Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Problems”, Applied. Soft Computing, vol. 11, pp. 3021–3031, 2011.
• [16] MS. Kıran, M. Gündüz, “A Novel Artificial Bee Colony-Based Algorithm for Solving the Numerical Optimization Problems”, International Journal of Innovative Computing. Information and Control, vol. 9, pp. 6107–6121, 2012.
• [17] SN. Omkar, J. Senthilnathproblems R. Khandelwal R, GN. Naik, S. Gopalakrishnan, “Artificial Bee Colony (ABC) for Multi-Objective Design Optimization of Composite Structures”, Applied. Soft Computing, vol. 11, pp. 489–499, 2011.
• [18] FEMA P646, “Guidelines for Design of Structures for Vertical Evacuation from Tsunamis”, (2008).