Skip to main content
Springer Nature Link
Log in

imBBO: An Improved Biogeography-Based Optimization Algorithm

  • Conference paper
  • First Online:
Green, Pervasive, and Cloud Computing (GPC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11204))

Included in the following conference series:

  • 730 Accesses

Abstract

Biogeography based Optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. However, its direct-copying-based migration and random mutation operators make it easily possess local exploitation ability. To enhance the performance of BBO, we propose an improved BBO algorithm called imBBO. A hybrid migration operation is designed to further improve the population diversity and enhance the algorithm exploration ability. Empirical results demonstrate that our imBBO effectively gains the high optimization performance by comparing with the original BBO and three BBO variants for 23 out of 30 CEC’2017 benchmarks. Moreover, our imBBO presents a faster convergence speed.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)

    Google Scholar 

  2. Arora, J.S.: Jan A. Snyman, practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms. Struct. Multi. Optim. 31(3), 249–249 (2006)

    Google Scholar 

  3. Awad, N., Ali, M., Liang, B., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Technical report (2016). http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2017

  4. Bhattacharya, A., Chattopadhyay, P.K.: Hybrid differential evolution with biogeography-based optimization algorithm for solution of economic emission load dispatch problems. Expert Syst. Appl. 38(11), 14001–14010 (2011)

    Google Scholar 

  5. Černý, V.: Thermodynamical approach to the traveling salesman problem: AN efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)

    MathSciNet  MATH  Google Scholar 

  6. Deep, K., Thakur, M.: A new mutation operator for real coded genetic algorithms. Appl. Math. Comput. 193(1), 211–230 (2007)

    MathSciNet  MATH  Google Scholar 

  7. Du, D., Simon, D., Ergezer, M.: Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In: Proceedings of International Conference on Systems, Man and Cybernetics, San Antonio, USA, pp. 997–1002 (2009)

    Google Scholar 

  8. Ekta, M.K.: Biogeography based optimization: a review. In: International Conference on Computing for Sustainable Global Development (2015)

    Google Scholar 

  9. Ellabib, I., Calamai, P.H., Basir, O.A.: Exchange strategies for multiple Ant Colony System. Inf. Sci. 177(5), 1248–1264 (2007)

    Google Scholar 

  10. Engelbrecht, A.P.: Computational Intelligence - An Introduction, 2nd edn. Wiley, Hoboken (2007)

    Google Scholar 

  11. Ergezer, M., Simon, D., Du, D.: Oppositional biogeography-based optimization. In: Proceedings of the IEEE International Conference on Systems, Manand Cybernetics, San Antonio, USA. pp. 1009–1014 (2009)

    Google Scholar 

  12. Feng, S.L., Zhu, Q.X., Gong, X.J., Zhong, S.: Hybridizing biogeography-based optimization with differential evolution for motif discovery problem. Appl. Mech. Mater. 457–458(4), 309–312 (2014)

    Google Scholar 

  13. Garg, V., Deep, K.: Performance of Laplacian biogeography-based optimization algorithm on CEC 2014 continuous optimization benchmarks and camera calibration problem. Swarm Evol. Comput. 27, 132–144 (2016)

    Google Scholar 

  14. Gong, W., Cai, Z., Ling, C.X.: DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft. Comput. 15(4), 645–665 (2010)

    Google Scholar 

  15. Gong, W., Cai, Z., Ling, C.X., Li, H.: A real-coded biogeography-based optimization with mutation. Appl. Math. Comput. 216(9), 2749–2758 (2010)

    MathSciNet  MATH  Google Scholar 

  16. Jadon, S.S., Tiwari, R., Sharma, H., Bansal, J.C.: Hybrid artificial bee colony algorithm with differential evolution. Appl. Soft Comput. 58, 11–24 (2017)

    Google Scholar 

  17. Kanoongo, S., Jain, P.: Blended biogeography based optimization for different economic load dispatch problem. In: Proceedings of the 25th International Conference on Electrical and Computer Engineering (CCECE), Montreal, QC, Canada, pp. 1–5 (2012)

    Google Scholar 

  18. Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72950-1_77

    MATH  Google Scholar 

  19. Li, X., Wang, J., Zhou, J., Yin, M.: A perturb biogeography based optimization with mutation for global numerical optimization. Appl. Math. Comput. 218(2), 598–609 (2011)

    MathSciNet  MATH  Google Scholar 

  20. Li, X., Yin, M.: Multi-operator based biogeography based optimization with mutation for global numerical optimization. Comput. Math Appl. 64(9), 2833–2844 (2012)

    MathSciNet  MATH  Google Scholar 

  21. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)

    Google Scholar 

  22. Lohokare, M.R., Panigrahi, B.K., Pattnaik, S.S., Devi, S., Mohapatra, A.: Neighborhood search-driven accelerated biogeography-based optimization for optimal load dispatch. IEEE Trans. Syst. Man Cybern. Part C 42(5), 641–652 (2012)

    Google Scholar 

  23. Ma, H.: An analysis of the equilibrium of migration models for biogeography-based optimization. Inf. Sci. 180(18), 3444–3464 (2010)

    MATH  Google Scholar 

  24. Ma, H., Simon, D.: Biogeography-based optimization with blended migration for constrained optimization problems. In: Proceedings of the International Conference on Genetic and Evolutionary Computation Conference (GECCO), Portland, Oregon, USA, pp. 417–418 (2010)

    Google Scholar 

  25. Ma, H., Simon, D.: Analysis of migration models of biogeography-based optimization using markov theory. Eng. Appl. AI 24(6), 1052–1060 (2011)

    Google Scholar 

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

    Google Scholar 

  27. Ma, H., Simon, D., Fei, M., Shu, X., Chen, Z.: Hybrid biogeography-based evolutionary algorithms. Eng. Appl. AI 30, 213–224 (2014)

    Google Scholar 

  28. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Google Scholar 

  29. O’Reilly, U.: Genetic programming II: automatic discovery of reusable programs. Artif. Life 1(4), 439–441 (1994)

    Google Scholar 

  30. Pholdee, N., Bureerat, S.: Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints. Adv. Eng. Softw. 75, 1–13 (2014)

    Google Scholar 

  31. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-31306-0

    MATH  Google Scholar 

  32. Rarick, R.A., Simon, D., Villaseca, F.E., Vyakaranam, B.: Biogeography-based optimization and the solution of the power flow problem. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, pp. 1003–1008 (2009)

    Google Scholar 

  33. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    MATH  Google Scholar 

  34. Shi, K., Yu, H., Fan, G., Luo, F.: iCPBBOCO: a combination evaluation algorithm based on the extensional BBO. In: Proceedings of International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, pp. 717–723 (2016)

    Google Scholar 

  35. Shi, K., Yu, H., Luo, F., Fan, G.: Multi-objective biogeography-based method to optimize virtual machine consolidation. In: Proceedings of 28th International Conference on Software Engineering and Knowledge Engineering (SEKE), Redwood City, San Francisco Bay, USA, pp. 225–230 (2016)

    Google Scholar 

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

    Google Scholar 

  37. Simon, D.: A dynamic system model of biogeography-based optimization. Appl. Soft Comput. 11(8), 5652–5661 (2011)

    Google Scholar 

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

    MathSciNet  Google Scholar 

  39. Singh, U., Singh, D., Kaur, C.: Hybrid differential evolution with biogeography based optimization for Yagi-Uda antenna design. In: Proceedings of the International Conference on Circuit, Power and Computing Technologies, pp. 1163–1167 (2015)

    Google Scholar 

  40. Storn, R., Price, K.V.: Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  42. Yao, X., Liu, Y.: Fast evolution strategies. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 149–161. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0014808

    Google Scholar 

  43. Zheng, Q., et al.: Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Futur. Gener. Comput. Syst. 54, 95–122 (2016)

    Google Scholar 

Download references

Acknowledgments

This work is partially supported by the NSF of China under grants No. 61772200, 61702334 and No. 61472139, Shanghai Pujiang Talent Program under grants No. 17PJ1401900, Shanghai Municipal Natural Science Foundation under Grants No. 17ZR1406900 and 17ZR1429700, Educational Research Fund of ECUST under Grant No. ZH1726108, the Collaborative Innovation Foundation of Shanghai Institute of Technology under Grants No. XTCX2016-20, the Opening Project of Key Lab of Information Network Security of Ministry of Public Security Under No. C17604, Key Lab of Information Network Security of Ministry of Public Security Under No. C17604.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Kai Shi, Huiqun Yu, Guisheng Fan & Xingguang Yang

  2. Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, 201112, China

    Kai Shi

  3. The Third Research Institute of the Ministry of Public Security, Shanghai, China

    Zheng Song

Authors
  1. Kai Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huiqun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guisheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingguang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zheng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Huiqun Yu or Guisheng Fan .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Shijian Li

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shi, K., Yu, H., Fan, G., Yang, X., Song, Z. (2019). imBBO: An Improved Biogeography-Based Optimization Algorithm. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15093-8_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15092-1

  • Online ISBN: 978-3-030-15093-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics