Research Article
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Year 2021, Volume: 5 Issue: 2, 81 - 88, 01.04.2021
https://doi.org/10.31127/tuje.693103

Abstract

References

  • Abro A G & Mohamad-Saleh J (2014). Enhanced probability-selection artificial bee colony algorithm for economic load dispatch: A comprehensive analysis. Engineering Optimization, 46(10), 1315–1330. DOI: 10.1080/0305215X.2013.836639
  • Akay B & Karaboga D (2015). A survey on the applications of artificial bee colony in signal, image, and video processing. Signal, Image and Video Processing, 9, 967–990. DOI: 10.1007/s11760-015-0758-4
  • Aldwairi M, Khamayseh Y & Al-Masri M (2015). Application of artificial bee colony for intrusion detection systems. Security and Communication Networks, 8(16), 2730–2740. DOI: 10.1002/sec.588
  • Apalak M K, Karaboga D & Akay B (2014). The Artificial Bee Colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates. Engineering Optimization, 46(3), 420–437. DOI: 10.1080/0305215X.2013.776551
  • Chen J, Li C & Yu W (2017). Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm. Advances in Engineering Research, 116, 689-695.
  • Chen S-M, Sarosh A & Dong Y-F (2012). Simulated annealing based artificial bee colony algorithm for global numerical optimization. Applied Mathematics and Computation, 219(8), 3575–3589. DOI: 10.1016/j.amc.2012.09.052
  • Cheng X & Jiang M (2012). An improved artificial bee colony algorithm based on Gaussian mutation and chaos disturbance. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 326–333. DOI: 10.1007/978-3-642-30976-2_39
  • Cuevas E, Zaldívar D, Pérez-Cisneros M, Sossa H & Osuna V (2013). Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Applied Soft Computing, 13(6), pp. 3047–3059. DOI: 10.1016/j.asoc.2012.09.020
  • Gao W & Liu S (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687–697. DOI: 10.1016/j.cor.2011.06.007
  • Han Y Y, Gong D & Sun X (2015). A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking. Engineering Optimization, 47(7), 927–946. DOI: 10.1080/0305215X.2014.928817
  • He X, Wang W, Jiang J & Xu L (2015). An improved artificial bee colony algorithm and its application to multi-objective optimal power flow. Energies, 8(4), 2412–2437. DOI: 10.3390/en8042412
  • Huang F, Wang L & Yang C (2016). A new improved artificial bee colony algorithm for ship hull form optimization. Engineering Optimization, 48(4), 672–686. DOI: 10.1080/0305215X.2015.1031660
  • Ismail M M & Baskaran K (2014). Hybrid lifting based image compression scheme using particle swarm optimization algorithm and artifical bee colony algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 3(1), 4899-4907.
  • Jia D, Duan X & Khan M K (2015). Modified artificial bee colony optimization with block perturbation strategy. Engineering Optimization, 47(5), 642–655. DOI: 10.1080/0305215X.2014.914189
  • Kang F, Li J & Ma Z (2011). Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences, 181(16), 3508–3531. DOI: 10.1016/j.ins.2011.04.024
  • Kang F, Li J & Ma Z (2013). An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Engineering Optimization, 45(2), 207–223. DOI: 10.1080/0305215X.2012.665451
  • Karaboga D (2005). An idea based on honey bee swarm for numerical optimization. Technical Report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 200, 1-10.
  • Karaboga D, Akay B & Ozturk C (2007). Artificial bee colony (ABC) optimization algorithm for training Feed-Forward neural networks. Modeling Decisions for Artificial Intelligence, Springer Berlin Heidelberg, pp. 318–329. DOI: 10.1007/978-3-540-73729-2_30
  • Karaboga D, Gorkemli B, Ozturk C & Karaboga N (2014). A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42, pp. 21–57. DOI: 10.1007/s10462-012-9328-0
  • Karaboga D & Ozturk C (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657. DOI: 10.1016/j.asoc.2009.12.025
  • Keles M K & Kilic U (2018). Artificial Bee Colony Algorithm for feature selection on SCADI Dataset. 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, 463–466. DOI: 10.1109/UBMK.2018.8566287
  • Liu Y, Ma L & Yang G (2017). A Survey of Artificial Bee Colony Algorithm. 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE, 1510–1515. DOI: 10.1109/CYBER.2017.8446301
  • Lozano M, García-Martínez C, Rodríguez F J & Trujillo H M (2017). Optimizing network attacks by artificial bee colony. Information Sciences, 377, 30–50. DOI: 10.1016/j.ins.2016.10.014
  • Shah H, Herawan T, Naseem R & Ghazali R (2014). Hybrid guided artificial bee colony algorithm for numerical function optimization. Lecture Notes in Computer Science, 8794(7). DOI: 10.1007/978-3-319-11857-4_23
  • Sun L, Chen T & Zhang Q (2018). An artificial bee colony algorithm with random location updating. Scientific Programming.
  • Wang S, Guo X & Liu J (2019). An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times. Engineering Optimization, 51(11), 1–18. DOI: 10.1080/0305215X.2018.1564918

Honey formation optimization: HFO

Year 2021, Volume: 5 Issue: 2, 81 - 88, 01.04.2021
https://doi.org/10.31127/tuje.693103

Abstract

In this paper, a new optimization framework, namely Honey Formation Optimization (HFO), is proposed. In contrary to the Artificial Bee Colony Optimization (ABC) variants in literature, the HFO considers food sources consisting of many components and model the honey formation inside bees as a process of mixing the components with their special enzymes during chewing up the food source. We believe that bees analyze the amounts of components inside the food source and attempt more to collect weaker (less amount) components to improve the honey formation process. Thus, each time a worker exploits a food source it selects a component in such a way that weaker components are more frequently selected. The approach requires decomposing the solution into components where each component is evaluated by a component fitness function. The honey formula maps the component fitness to honey amount and considered as the equivalence of the fitness function. The worker bee uses the fitness of the selected component to evaluate the food source and does local search only around the selected component. The HFO and ABC Frameworks are compared on the basis of 9 benchmark functions. The result shows that HFO performs better than the ABC. 

References

  • Abro A G & Mohamad-Saleh J (2014). Enhanced probability-selection artificial bee colony algorithm for economic load dispatch: A comprehensive analysis. Engineering Optimization, 46(10), 1315–1330. DOI: 10.1080/0305215X.2013.836639
  • Akay B & Karaboga D (2015). A survey on the applications of artificial bee colony in signal, image, and video processing. Signal, Image and Video Processing, 9, 967–990. DOI: 10.1007/s11760-015-0758-4
  • Aldwairi M, Khamayseh Y & Al-Masri M (2015). Application of artificial bee colony for intrusion detection systems. Security and Communication Networks, 8(16), 2730–2740. DOI: 10.1002/sec.588
  • Apalak M K, Karaboga D & Akay B (2014). The Artificial Bee Colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates. Engineering Optimization, 46(3), 420–437. DOI: 10.1080/0305215X.2013.776551
  • Chen J, Li C & Yu W (2017). Adaptive Image Enhancement Based on Artificial Bee Colony Algorithm. Advances in Engineering Research, 116, 689-695.
  • Chen S-M, Sarosh A & Dong Y-F (2012). Simulated annealing based artificial bee colony algorithm for global numerical optimization. Applied Mathematics and Computation, 219(8), 3575–3589. DOI: 10.1016/j.amc.2012.09.052
  • Cheng X & Jiang M (2012). An improved artificial bee colony algorithm based on Gaussian mutation and chaos disturbance. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 326–333. DOI: 10.1007/978-3-642-30976-2_39
  • Cuevas E, Zaldívar D, Pérez-Cisneros M, Sossa H & Osuna V (2013). Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Applied Soft Computing, 13(6), pp. 3047–3059. DOI: 10.1016/j.asoc.2012.09.020
  • Gao W & Liu S (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687–697. DOI: 10.1016/j.cor.2011.06.007
  • Han Y Y, Gong D & Sun X (2015). A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking. Engineering Optimization, 47(7), 927–946. DOI: 10.1080/0305215X.2014.928817
  • He X, Wang W, Jiang J & Xu L (2015). An improved artificial bee colony algorithm and its application to multi-objective optimal power flow. Energies, 8(4), 2412–2437. DOI: 10.3390/en8042412
  • Huang F, Wang L & Yang C (2016). A new improved artificial bee colony algorithm for ship hull form optimization. Engineering Optimization, 48(4), 672–686. DOI: 10.1080/0305215X.2015.1031660
  • Ismail M M & Baskaran K (2014). Hybrid lifting based image compression scheme using particle swarm optimization algorithm and artifical bee colony algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 3(1), 4899-4907.
  • Jia D, Duan X & Khan M K (2015). Modified artificial bee colony optimization with block perturbation strategy. Engineering Optimization, 47(5), 642–655. DOI: 10.1080/0305215X.2014.914189
  • Kang F, Li J & Ma Z (2011). Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences, 181(16), 3508–3531. DOI: 10.1016/j.ins.2011.04.024
  • Kang F, Li J & Ma Z (2013). An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Engineering Optimization, 45(2), 207–223. DOI: 10.1080/0305215X.2012.665451
  • Karaboga D (2005). An idea based on honey bee swarm for numerical optimization. Technical Report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 200, 1-10.
  • Karaboga D, Akay B & Ozturk C (2007). Artificial bee colony (ABC) optimization algorithm for training Feed-Forward neural networks. Modeling Decisions for Artificial Intelligence, Springer Berlin Heidelberg, pp. 318–329. DOI: 10.1007/978-3-540-73729-2_30
  • Karaboga D, Gorkemli B, Ozturk C & Karaboga N (2014). A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42, pp. 21–57. DOI: 10.1007/s10462-012-9328-0
  • Karaboga D & Ozturk C (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657. DOI: 10.1016/j.asoc.2009.12.025
  • Keles M K & Kilic U (2018). Artificial Bee Colony Algorithm for feature selection on SCADI Dataset. 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, 463–466. DOI: 10.1109/UBMK.2018.8566287
  • Liu Y, Ma L & Yang G (2017). A Survey of Artificial Bee Colony Algorithm. 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE, 1510–1515. DOI: 10.1109/CYBER.2017.8446301
  • Lozano M, García-Martínez C, Rodríguez F J & Trujillo H M (2017). Optimizing network attacks by artificial bee colony. Information Sciences, 377, 30–50. DOI: 10.1016/j.ins.2016.10.014
  • Shah H, Herawan T, Naseem R & Ghazali R (2014). Hybrid guided artificial bee colony algorithm for numerical function optimization. Lecture Notes in Computer Science, 8794(7). DOI: 10.1007/978-3-319-11857-4_23
  • Sun L, Chen T & Zhang Q (2018). An artificial bee colony algorithm with random location updating. Scientific Programming.
  • Wang S, Guo X & Liu J (2019). An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times. Engineering Optimization, 51(11), 1–18. DOI: 10.1080/0305215X.2018.1564918
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Zeki Yetgin 0000-0001-5918-6565

Mustafa Şamdan 0000-0003-4079-4565

Publication Date April 1, 2021
Published in Issue Year 2021 Volume: 5 Issue: 2

Cite

APA Yetgin, Z., & Şamdan, M. (2021). Honey formation optimization: HFO. Turkish Journal of Engineering, 5(2), 81-88. https://doi.org/10.31127/tuje.693103
AMA Yetgin Z, Şamdan M. Honey formation optimization: HFO. TUJE. April 2021;5(2):81-88. doi:10.31127/tuje.693103
Chicago Yetgin, Zeki, and Mustafa Şamdan. “Honey Formation Optimization: HFO”. Turkish Journal of Engineering 5, no. 2 (April 2021): 81-88. https://doi.org/10.31127/tuje.693103.
EndNote Yetgin Z, Şamdan M (April 1, 2021) Honey formation optimization: HFO. Turkish Journal of Engineering 5 2 81–88.
IEEE Z. Yetgin and M. Şamdan, “Honey formation optimization: HFO”, TUJE, vol. 5, no. 2, pp. 81–88, 2021, doi: 10.31127/tuje.693103.
ISNAD Yetgin, Zeki - Şamdan, Mustafa. “Honey Formation Optimization: HFO”. Turkish Journal of Engineering 5/2 (April 2021), 81-88. https://doi.org/10.31127/tuje.693103.
JAMA Yetgin Z, Şamdan M. Honey formation optimization: HFO. TUJE. 2021;5:81–88.
MLA Yetgin, Zeki and Mustafa Şamdan. “Honey Formation Optimization: HFO”. Turkish Journal of Engineering, vol. 5, no. 2, 2021, pp. 81-88, doi:10.31127/tuje.693103.
Vancouver Yetgin Z, Şamdan M. Honey formation optimization: HFO. TUJE. 2021;5(2):81-8.
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