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Modified Artificial Bee Colony Algorithm Based on Disruption Operator

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

Abstract

Artificial bee colony (ABC) algorithm has been proven to be an effective swarm intelligence-based algorithm to solve various numerical optimization problems. To improve the exploration and exploitation capabilities of ABC algorithm a new phase, namely disruption phase is introduced in the basic ABC. In disruption phase, disrupted operator in which the solutions are attracted or disrupted from the best solution based on the their respective distance from the best solution, is applied to all the solutions except the best solution. Further, the proposed strategy has been evaluated on 15 different benchmark functions and compared with basic ABC and two of its variants, namely modified ABC (MABC) and best so for ABC (BSFABC).

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References

  1. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (abc) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  2. Bansal, J.C., Sharma, H., Jadon, S.S.: Artificial bee colony algorithm: a survey. Int. J. Adv. Intell. Paradigms 5(1), 123–159 (2013)

    Google Scholar 

  3. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bansal, J.C., Sharma, H., Arya, K.V., Deep, K., Pant, M.: Self-adaptive artificial bee colony. Optimization 63(10), 1513–1532 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bansal, J.C., Sharma, H., Arya, K.V., Nagar, A.: Memetic search in artificial bee colony algorithm. Soft. Comput. 17(10), 1911–1928 (2013)

    Article  Google Scholar 

  6. Bansal, J.C., Sharma, H., Nagar, A., Arya, K.V.: Balanced artificial bee colony algorithm. Int. J. Artif. Intell. Soft Comput. 3(3), 222–243 (2013)

    Article  Google Scholar 

  7. Jadon, S.S., Bansal, J.C., Tiwari, R., Sharma, H.: Expedited artificial bee colony algorithm. In: Proceedings of the Third International Conference on Soft Computing for Problem Solving, pp. 787–800. Springer (2014)

    Google Scholar 

  8. Sharma, H., Bansal, J.C., Arya, K.V.: Opposition based lévy flight artificial bee colony. Memetic Comput. 5(3), 213–227 (2013)

    Article  Google Scholar 

  9. Sharma, H., Bansal, J.C., Arya, K.V.: Power law-based local search in artificial bee colony. Int. J. Artif. Intell. Soft Comput. 4(2/3), 164–194 (2014)

    Article  Google Scholar 

  10. Sharma, H., Bansal, J.C., Arya, K.V., Deep, K.: Dynamic swarm artificial bee colony algorithm. Int. J. Appl. Evol. Comput. (IJAEC) 3(4), 19–33 (2012)

    Google Scholar 

  11. Sarafrazi, S., Nezamabadi-Pour, H., Saryazdi, S.: Disruption: a new operator in gravitational search algorithm. Scientia Iranica 18(3), 539–548 (2011)

    Article  Google Scholar 

  12. Ding, G.Y., Liu, H., He, X.Q.: A novel disruption operator in particle swarm optimization. Appl. Mech. Mater. 380, 1216–1220 (2013)

    Article  Google Scholar 

  13. Liu, H., Ding, G., Sun, H.: An improved opposition-based disruption operator in gravitational search algorithm. In: 2012 Fifth International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 123–126. IEEE (2012)

    Google Scholar 

  14. Chen, T.-Y., Chi, T.-M.: On the improvements of the particle swarm optimization algorithm. Adv. Eng. Softw. 41(2), 229–239 (2010)

    Article  MATH  Google Scholar 

  15. Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. 11(2), 2888–2901 (2011)

    Article  Google Scholar 

  16. Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)

    Article  Google Scholar 

  17. Bansal, J.C., Sharma, H.: Cognitive learning in differential evolution and its application to model order reduction problem for single-input single-output systems. Memetic Comput. 4(3), 209–229 (2012)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Rajasthan Technical University, Kota, Rajasthan, India

    Nirmala Sharma &  Harish Sharma

  2. Government Engineering College, Jhalawar, Rajasthan, India

    Ajay Sharma

  3. South Asian University, New Delhi, India

    Jagdish Chand Bansal

Authors
  1. Nirmala Sharma
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  2. Harish Sharma
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  3. Ajay Sharma
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  4. Jagdish Chand Bansal
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Corresponding author

Correspondence to Nirmala Sharma .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Nirmala Sharma, Harish Sharma, Ajay Sharma, Bansal, J.C. (2016). Modified Artificial Bee Colony Algorithm Based on Disruption Operator. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_79

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_79

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

  • eBook Packages: EngineeringEngineering (R0)

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