Skip to main content
SpringerLink
Log in

A cooperative coevolutionary biogeography-based optimizer

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

With its unique migration operator and mutation operator, Biogeography-Based Optimization (BBO), which simulates migration of species in natural biogeography, is different from existing evolutionary algorithms, but it has shortcomings such as poor convergence precision and slow convergence speed when it is applied to solve complex optimization problems. Therefore, we put forward a Cooperative Coevolutionary Biogeography-Based Optimizer (CBBO) in this paper. In CBBO, the whole population is divided into multiple sub-populations first, and then each subpopulation is evolved with an improved BBO separately. The fitness evaluation of habitats of a subpopulation is conducted by constructing context vectors with selected habitats from other sub-populations. Our CBBO tests are based on 13 benchmark functions and are also compared with several other evolutionary algorithms. Experimental results demonstrate that CBBO is able to achieve better results than other evolutionary algorithms on most of the benchmark functions.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Boussaid I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117

    Article  MathSciNet  Google Scholar 

  2. Holand JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  3. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

    Article  Google Scholar 

  4. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, no 2, pp 1942–1948

  5. Fogel DB (1991) System identification through simulated evolution: a machine learning approach to modeling. Ginn Press, Needham Heights

    Google Scholar 

  6. Fogel DB (1993) Applying evolutionary programming to selected traveling salesman problems. Cybern Syst 24:27–36

    Article  MathSciNet  Google Scholar 

  7. Krause J, Cordeiro J, Parpinelli RS, Lopes HS (2013) A survey of swarm algorithms applied to discrete optimization problems. Swarm intelligence and bio-inspired computation: theory and applications elsevier science and technology books, pp 169–191

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

    Article  Google Scholar 

  9. Potter MA, De Jong KA (1994) A cooperative coevolutionary approach to function optimization. In: Parallel problem solving from nature—PPSN III, pp 249–257

  10. Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239

    Article  Google Scholar 

  11. Liu Y, Yao X, Zhao Q, Higuchi T (2001) Scaling up fast evolutionary programming with cooperative coevolution. In: IEEE congress on evolutionary computation. CEC 2001, vol 2, pp 1101–1108

  12. Boga DK, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  Google Scholar 

  13. Zhang P, Liu H, Ding Y (2014) Dynamic bee colony algorithm based on multi-species co-evolution. Appl Intell 40(3):427– 440

    Article  Google Scholar 

  14. Simon D, Ergezer M, Du D (2009) Population distributions in biogeography-based optimization algorithms with elitism. In: Proceedings of the IEEE International Conference on Systems Man and Cybernetics, pp 991–996

  15. Sinha A, Das S, Panigrahi BK (2011) A linear state-space analysis of the migration model in an island biogeography system. IEEE Trans Syst Man Cybern, Part A Syst Hum 41(2):331–337

    Article  Google Scholar 

  16. Simon D, Ergezer M, Du D, Rarick R (2011) Markov models for biogeography-based optimization. IEEE Trans Syst Man Cybern, Part B Cybern 41(1):299–306

    Article  Google Scholar 

  17. Guo W, Wang L, Wu Q (2014) An analysis of the migration rates for biogeography-based optimization. Inf Sci 254:111– 140

    Article  MathSciNet  Google Scholar 

  18. 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

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

    Article  MATH  Google Scholar 

  20. Bhattacharya A, Chattopadhyay PK (2010) Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Trans Power Syst 25(4):1955– 1964

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Ergezer M, Simon D, Du D (2009) Oppositional biogeography-based optimization. In: IEEE conference on Syst Man Cybern. SMC 2009, pp 1009–1014

  23. Tizhoosh H (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of international conference on computational intelligence for modelling control and automation, vol 1, pp 695–701

  24. Ventresca M, Tizhoosh H (2006) Improving the convergence of back propagation by opposite transfer functions. In: IEEE international joint conference on neural networks, pp 9527– 9534

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

    Article  MATH  MathSciNet  Google Scholar 

  26. Zheng YJ, Ling HF, Wu XB, Xue JY (2013) Localized biogeography-based optimization. Soft Comput 18(11):2323–2334

  27. Ma HP, Ruan XY, Pan ZX (2012) Handling multiple objectives with biogeography-based optimization. Int J Autom Comput 9(1):30–36

    Article  Google Scholar 

  28. Zheng XW, Gao KG, Wang XG, Ma CZ (2014) A multi-objective biogeography-based optimization with mean value migration operator. In: Frontier and future development of information technology in medicine and education. Springer, Netherlands, pp 679–686

  29. Gupta S, Arora A, Panchal VK, Goel S (2011) Extended biogeography based optimization for natural terrain feature classification from satellite remote sensing images. Contemporary computing. Springer, Berlin Heidelberg, pp 262–269

  30. Rarick R, Simon D, Villaseca FE, Vyakaranam B (2009) Biogeography-based optimization and the solution of the power flow problem. In: IEEE conference on Syst Man Cybern. SMC 2009, pp 1003–1008

  31. Boussaid I, Chatterjee A, Siarry P, Ahmed-Nacer M (2013) Hybrid BBO-DE algorithms for fuzzy entropy-based thresholding. In: Computational intelligence in image processing. Springer, Berlin Heidelberg, pp 37–69

  32. Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29

    Article  Google Scholar 

  33. Barbosa HJ (1999) A coevolutionary genetic algorithm for constrained optimization. In: Proceedings of the 1999 congress on evolutionary computation. CEC 99, vol 3

  34. Lohn JD, Kraus WF, Haith GL (2002) Comparing a coevolutionary genetic algorithm for multiobjective optimization. In: Proceedings of the 2002 congress on evolutionary computation. CEC 2002, vol 2, pp 1157–1162

  35. Tan KC, Yang YJ, Goh CK (2006) A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Trans Evol Comput 10(5):527–549

    Article  Google Scholar 

  36. Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999

    Article  MATH  MathSciNet  Google Scholar 

  37. Omidvar MN, Li X, Yang Z, Yao X (2010) Cooperative co-evolution for large scale optimization through more frequent random grouping. In: IEEE congress on evolutionary computation. CEC 2010, pp 1–8

  38. Zheng X, Liu H (2010) A scalable coevolutionary multi-objective particle swarm optimizer. Int J Comput Intell Syst 3(5):590– 600

    Article  Google Scholar 

  39. Hasanzadeh M, Meybodi M R, Ebadzadeh M M (2013) Adaptive cooperative particle swarm optimizer. Appl Intell 39(2):397– 420

    Article  Google Scholar 

  40. Wiegand RP, Liles WC, De Jong KA (2001) An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 2611:1235– 1245

  41. Yu TL, Goldberg DE, Sastry K, Lima CF, Pelikan M (2009) Dependency structure matrix, genetic algorithms, and effective recombination. Evol Comput 17:595–626

    Article  Google Scholar 

  42. Omidvar MN, Li X, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378–393

Download references

Acknowledgments

We are grateful for the support of the National Natural Science Foundation of China (61373149, 61272094), the Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (BS2010DX033) and a Project of Shandong Province Higher Educational Science and Technology Program (J10LG08).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, People’s Republic of China

    Xiang-wei Zheng, Dian-jie Lu, Xiao-guang Wang & Hong Liu

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250014, People’s Republic of China

    Xiang-wei Zheng, Dian-jie Lu, Xiao-guang Wang & Hong Liu

Authors
  1. Xiang-wei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dian-jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-guang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang-wei Zheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, Xw., Lu, Dj., Wang, Xg. et al. A cooperative coevolutionary biogeography-based optimizer. Appl Intell 43, 95–111 (2015). https://doi.org/10.1007/s10489-014-0627-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-014-0627-9

Keywords

Search

Navigation