Skip to main content
Log in

A novel disruption in biogeography-based optimization with application to optimal power flow problem

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Biogeography-based optimization (BBO) is an emerging meta-heuristic algorithm. Due to ease of implementation and very few user-dependent parameters, BBO gained popularity among researchers. The performance of BBO is highly dependent on its two operators, migration and mutation. The performance of BBO can be significantly improved by either modifying these operators or by introducing a new operator into it. This paper proposes a new operator, namely the disruption operator to improve the capability of exploration and exploitation in BBO. The proposed DisruptBBO (DBBO) has been tested on well-known benchmark problems and compared with various versions of BBO and other state-of-the-art metaheuristics. The experimental results and statistical analyses confirm the superior performance of the proposed DBBO in solving various nonlinear complex optimization problems. The proposed algorithm has also been applied to the optimal power flow optimization problem from the electrical engineering background.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Rezaei Adaryani M, Karami A (2013) Artificial bee colony algorithm for solving multi-objective optimal power flow problem. Int J Electr Power Energy Syst 53:219–230

    Article  Google Scholar 

  2. Amjady N, Fatemi H, Zareipour H (2012) Solution of optimal power flow subject to security constraints by a new improved bacterial foraging method. IEEE Trans Power Syst 27(3):1311–1323

    Article  Google Scholar 

  3. Bansal JC (2016) Modified blended migration and polynomial mutation in biogeography-based optimization. In Harmony Search Algorithm . Springer, pp 217–225

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

    Article  MathSciNet  MATH  Google Scholar 

  5. Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memetic Comput 6(1):31–47

    Article  Google Scholar 

  6. Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188(1):895–911

    MathSciNet  MATH  Google Scholar 

  7. Feng Q, Liu S, Zhang J, Yang G, Yong L (2014) Biogeography-based optimization with improved migration operator and self-adaptive clear duplicate operator. Appl Intell 41(2):563– 581

    Article  Google Scholar 

  8. Garg V, Deep K (2015) A state-of-the-art review of biogeography-based optimization. Proceedings of Fourth International Conference on Soft Computing for Problem Solving, pages 533–549. Springer

  9. Guo W, Chen M, Wang L, Mao Y, Wu Q (2016) A survey of biogeography-based optimization. Neural Comput & Applic:1–18

  10. Guo W, Chen M, Wang L, Wu Q (2015) Backtracking biogeography-based optimization for numerical optimization and mechanical design problems. Appl Intell:1–10

  11. Harwit M (2006) Astrophysical concepts. Springer Science & Business Media

  12. Jana ND, Sil J (2015) Levy distributed parameter control in differential evolution for numerical optimization. Natural Comput:1–14

  13. Liu H, Ding G, Wang B (2014) Bare-bones particle swarm optimization with disruption operator. Appl Math Comput 238:106–122

    MathSciNet  MATH  Google Scholar 

  14. Lohokare MR, Pattnaik SS, Panigrahi BK, Das S (2013) Accelerated biogeography-based optimization with neighborhood search for optimization. Appl Soft Comput 13(5):2318– 2342

    Article  Google Scholar 

  15. Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Intell 24(3):517–525

    Article  Google Scholar 

  16. Ma L, Hu K, Zhu Y, Chen H (2015) A hybrid artificial bee colony optimizer by combining with life-cycle, powells search and crossover. Appl Math Comput 252:133–154

    MATH  Google Scholar 

  17. MacArthur RH, Wilson EO (1967) The theory of island biogeography, vol 1. Princeton University Press

  18. Niknam T, Narimani MR, Jabbari M, Malekpour AR (2011) A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy 36(11):6420–6432

    Article  Google Scholar 

  19. Ongsakul W, Tantimaporn T (2006) Optimal power flow by improved evolutionary programming. Electr Power Compon Syst 34(1):79–95

    Article  Google Scholar 

  20. Riget J, Vesterstrøm JS (2002) A diversity-guided particle swarm optimizer-the arpso. Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Tech. Rep, vol 2

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

    Article  Google Scholar 

  22. Sayah S, Zehar K (2008) Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers Manag 49(11):3036–3042

    Article  Google Scholar 

  23. Sharma H, Bansal JC, Arya KV (2014) Self balanced differential evolution. J Comput Sci 5(2):312–323

    Article  Google Scholar 

  24. Shin Y-B, Kita E (2014) Search performance improvement of particle swarm optimization by second best particle information. Appl Math Comput 246:346–354

    MathSciNet  MATH  Google Scholar 

  25. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  26. Simon D, Omran MGH, Clerc M (2014) Linearized biogeography-based optimization with re-initialization and local search. Inf Sci 267:140–157

    Article  MathSciNet  Google Scholar 

  27. Vaisakh K, Srinivas LR (2011) Evolving ant direction differential evolution for opf with non-smooth cost functions. Eng Appl Artif Intell 24(3):426–436

    Article  Google Scholar 

  28. Wang G-G, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9):2454–2462

    Article  MathSciNet  Google Scholar 

  29. Xiong G, Shi D, Duan X (2014) Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning. Comput Oper Res 41:125–139

    Article  MATH  Google Scholar 

Download references

Acknowledgment

The second author acknowledges the funding from South Asian University, New Delhi, India to carry out this research. Both authors also acknowledges anonymous reviewers Prof. Chander Mohan, IIT Roorkee and Dr. Ashok Pal, Chandigarh University for their intensive reviews.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jagdish Chand Bansal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bansal, J.C., Farswan, P. A novel disruption in biogeography-based optimization with application to optimal power flow problem. Appl Intell 46, 590–615 (2017). https://doi.org/10.1007/s10489-016-0848-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0848-1

Keywords

Navigation