Advertisement

Applied Intelligence

, Volume 43, Issue 1, pp 95–111 | Cite as

A cooperative coevolutionary biogeography-based optimizer

  • Xiang-wei ZhengEmail author
  • Dian-jie Lu
  • Xiao-guang Wang
  • Hong Liu
Article

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.

Keywords

Biogeography-Based Optimization (BBO) Cooperation Coevolution Decomposition Context vector 

Notes

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).

References

  1. 1.
    Boussaid I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117CrossRefMathSciNetGoogle Scholar
  2. 2.
    Holand JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  3. 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):29–41CrossRefGoogle Scholar
  4. 4.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, no 2, pp 1942–1948Google Scholar
  5. 5.
    Fogel DB (1991) System identification through simulated evolution: a machine learning approach to modeling. Ginn Press, Needham HeightsGoogle Scholar
  6. 6.
    Fogel DB (1993) Applying evolutionary programming to selected traveling salesman problems. Cybern Syst 24:27–36CrossRefMathSciNetGoogle Scholar
  7. 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 169–191Google Scholar
  8. 8.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRefGoogle Scholar
  9. 9.
    Potter MA, De Jong KA (1994) A cooperative coevolutionary approach to function optimization. In: Parallel problem solving from nature—PPSN III, pp 249–257Google Scholar
  10. 10.
    Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRefGoogle Scholar
  11. 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 1101–1108Google Scholar
  12. 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):459–471CrossRefMathSciNetGoogle Scholar
  13. 13.
    Zhang P, Liu H, Ding Y (2014) Dynamic bee colony algorithm based on multi-species co-evolution. Appl Intell 40(3):427– 440CrossRefGoogle Scholar
  14. 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 991–996Google Scholar
  15. 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):331–337CrossRefGoogle Scholar
  16. 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):299–306CrossRefGoogle Scholar
  17. 17.
    Guo W, Wang L, Wu Q (2014) An analysis of the migration rates for biogeography-based optimization. Inf Sci 254:111– 140CrossRefMathSciNetGoogle Scholar
  18. 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):563–581Google Scholar
  19. 19.
    Ma H (2010) An analysis of the equilibrium of migration models for biogeography-based optimization. Inf Sci 180(18):3444– 3464CrossRefzbMATHGoogle Scholar
  20. 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– 1964CrossRefGoogle Scholar
  21. 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):645–665CrossRefGoogle Scholar
  22. 22.
    Ergezer M, Simon D, Du D (2009) Oppositional biogeography-based optimization. In: IEEE conference on Syst Man Cybern. SMC 2009, pp 1009–1014Google Scholar
  23. 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 695–701Google Scholar
  24. 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– 9534Google Scholar
  25. 25.
    Gong W, Cai Z, Ling CX, Li H (2010) A real-coded biogeography-based optimization with mutation. Appl Math Comput 216(9):2749–2758CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    Zheng YJ, Ling HF, Wu XB, Xue JY (2013) Localized biogeography-based optimization. Soft Comput 18(11):2323–2334Google Scholar
  27. 27.
    Ma HP, Ruan XY, Pan ZX (2012) Handling multiple objectives with biogeography-based optimization. Int J Autom Comput 9(1):30–36CrossRefGoogle Scholar
  28. 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 679–686Google Scholar
  29. 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 262–269Google Scholar
  30. 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 1003–1008Google Scholar
  31. 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 37–69Google Scholar
  32. 32.
    Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29CrossRefGoogle Scholar
  33. 33.
    Barbosa HJ (1999) A coevolutionary genetic algorithm for constrained optimization. In: Proceedings of the 1999 congress on evolutionary computation. CEC 99, vol 3Google Scholar
  34. 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 1157–1162Google Scholar
  35. 35.
    Tan KC, Yang YJ, Goh CK (2006) A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Trans Evol Comput 10(5):527–549CrossRefGoogle Scholar
  36. 36.
    Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999CrossRefzbMATHMathSciNetGoogle Scholar
  37. 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 1–8Google Scholar
  38. 38.
    Zheng X, Liu H (2010) A scalable coevolutionary multi-objective particle swarm optimizer. Int J Comput Intell Syst 3(5):590– 600CrossRefGoogle Scholar
  39. 39.
    Hasanzadeh M, Meybodi M R, Ebadzadeh M M (2013) Adaptive cooperative particle swarm optimizer. Appl Intell 39(2):397– 420CrossRefGoogle Scholar
  40. 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– 1245Google Scholar
  41. 41.
    Yu TL, Goldberg DE, Sastry K, Lima CF, Pelikan M (2009) Dependency structure matrix, genetic algorithms, and effective recombination. Evol Comput 17:595–626CrossRefGoogle Scholar
  42. 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):378–393Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Xiang-wei Zheng
    • 1
    • 2
    Email author
  • Dian-jie Lu
    • 1
    • 2
  • Xiao-guang Wang
    • 1
    • 2
  • Hong Liu
    • 1
    • 2
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanPeople’s Republic of China
  2. 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyJinanPeople’s Republic of China

Personalised recommendations