Skip to main content
SpringerLink
Log in

Artificial Bee Colony as a Frontier in Evolutionary Optimization: A Survey

  • Conference paper
  • First Online:
Advances in Computer and Computational Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 553))

Abstract

Artificial Bee Colony (ABC) algorithm is now a long-familiar example of Swarm Intelligence. It has been consistently drawing the attention of research scholars since last decade. The adept performance of ABC algorithm has already been proved in various researches. Hence this algorithm has been used in wide variety of applications, spanning almost all aspects of engineering optimization. This manuscript details out some of the application areas of ABC algorithm in a concise way and it aims to provide a bird eye view of various application areas for the beginner researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Christian Blum and Xiaodong Li. Swarm intelligence in optimization. Springer, 2008.

    Google Scholar 

  2. James Kennedy, James F Kennedy, Russell C Eberhart, and Yuhui Shi. Swarm intelligence. Morgan Kaufmann, 2001.

    Google Scholar 

  3. Marco Dorigo, Vittorio Maniezzo, Alberto Colorni, and Vittorio Maniezzo. Positive feedback as a search strategy. 1991.

    Google Scholar 

  4. Russ C Eberhart and James Kennedy. A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, volume 1, pages 39–43. New York, NY, 1995.

    Google Scholar 

  5. Dervis Karaboga. An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, 2005.

    Google Scholar 

  6. Xin-She Yang. A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010), pages 65–74. Springer, 2010.

    Google Scholar 

  7. Kevin M Passino. Bacterial foraging optimization. Innovations and Developments of Swarm Intelligence Applications, page 219, 2012.

    Google Scholar 

  8. Divya Kumar, Divya Kashyap, KK Mishra, and AK Mishra. Routing path determination using qos metrics and priority based evolutionary optimization. In High Performance Computing and Communications (HPCC), 2011 IEEE 13th International Conference on, pages 615–621. IEEE, 2011.

    Google Scholar 

  9. Divya Kumar and Krishn Kumar Mishra. Incorporating logic in artificial bee colony (abc) algorithm to solve first order logic problems: The logical abc. In Knowledge and Smart Technology (KST), 2015 7th International Conference on, pages 65–70. IEEE, 2015.

    Google Scholar 

  10. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz. Swarm intelligence: from natural to artificial systems. Number 1. Oxford university press, 1999.

    Google Scholar 

  11. Dervis Karaboga, Bahriye Akay, and Celal Ozturk. Artificial bee colony (abc) optimization algorithm for training feed-forward neural networks. In Modeling decisions for artificial intelligence, pages 318–329. Springer, 2007.

    Google Scholar 

  12. Valery Tereshko. Reaction-diffusion model of a honeybee colony’s foraging behaviour. In Parallel Problem Solving from Nature PPSN VI, pages 807–816. Springer, 2000.

    Google Scholar 

  13. Agoston E Eiben and Cornelis A Schippers. On evolutionary exploration and exploitation. Fundamenta Informaticae, 35(1–4):35–50, 1998.

    Google Scholar 

  14. Nguyen Tung Linh and Nguyen Quynh Anh. Application artificial bee colony algorithm (abc) for reconfiguring distribution network. In Computer Modeling and Simulation, 2010. ICCMS’10. Second International Conference on, volume 1, pages 102–106. IEEE, 2010.

    Google Scholar 

  15. Kursat Ayan and Ulas Kilic. Artificial bee colony algorithm solution for optimal reactive power flow. Applied Soft Computing, 12(5):1477–1482, 2012.

    Google Scholar 

  16. B Yao, C Yang, J Hu, and B Yu. The optimization of urban subway routes based on artificial bee colony algorithm. Key technologies of railway engineering high speed railway, heavy haul railway and urban rail transit. Beijing Jiaotong University, Beijing, pages 747–751, 2010.

    Google Scholar 

  17. Ali Hadidi, Sina Kazemzadeh Azad, and Saeid Kazemzadeh Azad. Structural optimization using artificial bee colony algorithm. In 2nd international conference on engineering optimization, 2010.

    Google Scholar 

  18. David L Gonzalez-Alvarez, Miguel A Vega-Rodriguez, Juan A Gomez-Pulido, and Juan M Sanchez-Perez. Finding motifs in dna sequences applying a multiobjective artificial bee colony (moabc) algorithm. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pages 89–100. Springer, 2011.

    Google Scholar 

  19. Yudong Zhang and Lenan Wu. Artificial bee colony for two dimensional protein folding. Advances in Electrical Engineering Systems, 1(1):19–23, 2012.

    Google Scholar 

  20. Dervis Karaboga and Bahriye Basturk. Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In Foundations of Fuzzy Logic and Soft Computing, pages 789–798. Springer, 2007.

    Google Scholar 

  21. D. Kumar and K.K. Mishra. Incorporating logic in artificial bee colony (abc) algorithm to solve first order logic problems: The logical abc. In Knowledge and Smart Technology (KST), 2015 7th International Conference on, pages 65–70, Jan 2015.

    Google Scholar 

  22. Dervis Karaboga and Celal Ozturk. A novel clustering approach: Artificial bee colony (abc) algorithm. Applied Soft Computing, 11(1):652–657, 2011.

    Google Scholar 

  23. M. Lichman. UCI machine learning repository, 2013.

    Google Scholar 

  24. Tirimula Rao Benala, Sathya Durga Jampala, SH Villa, and Bhargavi Konathala. A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pages 1071–1076. IEEE, 2009.

    Google Scholar 

  25. Chidambaram Chidambaram and Heitor Silverio Lopes. An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. International Journal of Natural Computing Research (IJNCR), 1(2):54–70, 2010.

    Google Scholar 

  26. Dervis Karaboga and Beyza Gorkemli. A combinatorial artificial bee colony algorithm for traveling salesman problem. In Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on, pages 50–53. IEEE, 2011.

    Google Scholar 

  27. Alok Singh. An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Applied Soft Computing, 9(2):625–631, 2009.

    Google Scholar 

  28. Dervis Karaboga, Selcuk Okdem, and Celal Ozturk. Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7):847–860, 2012.

    Google Scholar 

  29. Celal Ozturk, Dervis Karaboga, and Beyza Gorkemli. Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm. Sensors, 11(6):6056–6065, 2011.

    Google Scholar 

  30. F Ghareh Mohammadi and M Saniee Abadeh. Image steganalysis using a bee colony based feature selection algorithm. Engineering Applications of Artificial Intelligence, 31:35–43, 2014.

    Google Scholar 

  31. Pei-Wei Tsai, Muhammad Khurram Khan, Jeng-Shyang Pan, and Bin-Yih Liao. Interactive artificial bee colony supported passive continuous authentication system. Systems Journal, IEEE, 8(2):395–405, 2014.

    Google Scholar 

  32. Y Delican, RA Vural, and T Yildirim. Artificial bee colony optimization based cmos inverter design considering propagation delays. In Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD), 2010 XIth International Workshop on, pages 1–5. IEEE, 2010.

    Google Scholar 

  33. VJ Manoj and Elizabeth Elias. Artificial bee colony algorithm for the design of multiplier-less nonuniform filter bank transmultiplexer. Information Sciences, 192:193–203, 2012.

    Google Scholar 

  34. Ali Akdagli, Mustafa Berkan Bicer, and Seda Ermis. A novel expression for resonant length obtained by using artificial bee colony algorithm in calculating resonant frequency of c-shaped compact microstrip antennas. Turkish Journal of Electrical Engineering & Computer Sciences, 19(4):597–606, 2011.

    Google Scholar 

  35. A Toktas, MB Bicer, A Akdagli, and A Kayabasi. Simple formulas for calculating resonant frequencies of c and h shaped compact microstrip antennas obtained by using artificial bee colony algorithm. Journal of Electromagnetic Waves and Applications, 25(11–12):1718–1729, 2011.

    Google Scholar 

  36. Bahriye Akay and Dervis Karaboga. A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 192:120–142, 2012.

    Google Scholar 

  37. Dervis Karaboga and Bahriye Basturk. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. Journal of global optimization, 39(3):459–471, 2007.

    Google Scholar 

  38. Malek Alzaqebah and Salwani Abdullah. Hybrid artificial bee colony search algorithm based on disruptive selection for examination timetabling problems. In COCOA, pages 31–45. Springer, 2011.

    Google Scholar 

  39. Changsheng Zhang, Dantong Ouyang, and Jiaxu Ning. An artificial bee colony approach for clustering. Expert Systems with Applications, 37(7):4761–4767, 2010.

    Google Scholar 

  40. WY Szeto, Yongzhong Wu, and Sin C Ho. An artificial bee colony algorithm for the capacitated vehicle routing problem. European Journal of Operational Research, 215(1):126–135, 2011.

    Google Scholar 

  41. NK Garg, Shimpi Singh Jadon, Harish Sharma, and DK Palwalia. Gbest-artificial bee colony algorithm to solve load flow problem. In Proceedings of the Third International Conference on Soft Computing for Problem Solving, pages 529–538. Springer, 2014.

    Google Scholar 

  42. S Hemamalini and Sishaj P Simon. Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electric Power Components and Systems, 38(7):786–803, 2010.

    Google Scholar 

  43. Preetha Bhattacharjee, Pratyusha Rakshit, Indrani Goswami, Amit Konar, and Atulya K Nagar. Multi-robot path-planning using artificial bee colony optimization algorithm. In Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on, pages 219–224. IEEE, 2011.

    Google Scholar 

  44. Surender Singh Dahiya, Jitender Kumar Chhabra, and Shakti Kumar. Application of artificial bee colony algorithm to software testing. In Software Engineering Conference (ASWEC), 2010 21st Australian, pages 149–154. IEEE, 2010.

    Google Scholar 

  45. D Jeya Mala, V Mohan, and M Kamalapriya. Automated software test optimisation framework-an artificial bee colony optimisation-based approach. Software, IET, 4(5):334–348, 2010.

    Google Scholar 

  46. R Srinivasa Rao, SVL Narasimham, and M Ramalingaraju. Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm. International Journal of Electrical Power and Energy Systems Engineering, 1(2):116–122, 2008.

    Google Scholar 

  47. Nurhan Karaboga. A new design method based on artificial bee colony algorithm for digital iir filters. Journal of the Franklin Institute, 346(4):328–348, 2009.

    Google Scholar 

  48. Yudong Zhang, Lenan Wu, and Shuihua Wang. Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Progress in Electromagnetics Research, 116:65–79, 2011.

    Google Scholar 

  49. Jun-Qing Li, Quan-Ke Pan, and Kai-Zhou Gao. Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. The International Journal of Advanced Manufacturing Technology, 55(9–12):1159–1169, 2011.

    Google Scholar 

  50. Quan-Ke Pan, M Fatih Tasgetiren, Ponnuthurai N Suganthan, and Tay Jin Chua. A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information sciences, 181(12):2455–2468, 2011.

    Google Scholar 

  51. Jing Yao and Ju-hou He. Load balancing strategy of cloud computing based on artificial bee algorithm. In Computing Technology and Information Management (ICCM), 2012 8th International Conference on, volume 1, pages 185–189. IEEE, 2012.

    Google Scholar 

  52. Tsung-Jung Hsieh, Hsiao-Fen Hsiao, and Wei-Chang Yeh. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied soft computing, 11(2):2510–2525, 2011.

    Google Scholar 

  53. Dusan Teodorovic and Mauro Dellorco. Bee colony optimization–a cooperative learning approach to complex transportation problems. In Advanced OR and AI Methods in Transportation: Proceedings of 16th Mini–EURO Conference and 10th Meeting of EWGT (13–16 September 2005).–Poznan: Publishing House of the Polish Operational and System Research, pages 51–60, 2005.

    Google Scholar 

  54. Bahriye Akay and Dervis Karaboga. Solving integer programming problems by using artificial bee colony algorithm. In AI* IA 2009: Emergent Perspectives in Artificial Intelligence, pages 355–364. Springer, 2009.

    Google Scholar 

  55. Dervis Karaboga and Bahriye Basturk. On the performance of artificial bee colony (abc) algorithm. Applied soft computing, 8(1):687–697, 2008.

    Google Scholar 

  56. Dervis Karaboga and Bahriye Akay. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1):108–132, 2009.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India

    Divya Kumar & Krishn K. Mishra

Authors
  1. Divya Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krishn K. Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Divya Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Missouri, Columbia, Missouri, USA

    Sanjiv K. Bhatia

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Vivek Kumar Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Kumar, D., Mishra, K.K. (2017). Artificial Bee Colony as a Frontier in Evolutionary Optimization: A Survey. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_50

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3770-2_50

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3769-6

  • Online ISBN: 978-981-10-3770-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation