Skip to main content

Advertisement

Springer Nature Link
Log in

A survey of biogeography-based optimization

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Optimization is a classical issue and in many areas that are bound up with people’s daily life. In current decades, with the development of human civilization and industry society, many complicated optimization problems are raised. In the meantime, corresponding novel approaches are constantly proposed for solving these problems. One of them is meta-heuristics, which is inspired from natural phenomena and contains many kinds of algorithms. The classical meta-heuristic algorithms have exhibited their superiority in dealing optimization problems, especially for specific problems such as combinatorial optimization. As a novel meta-heuristic algorithm, biogeography-based optimization (BBO), inspired from the science of biogeography, has its own characteristics and exhibits a huge potential in computation and optimization. According to current investigations and analysis on this algorithm, it has not only achieved a great success in numerical optimization problems, but also been implemented in various kinds of applications, and drawn worldwide attentions. In this paper, we present a survey for this algorithm. First, we introduce the basic operators of BBO, including migration and mutation. For migration operator, it mimics species migration among islands, which provides a recombination way for candidate solutions to interact with each other so that the whole population can be improved. Besides linear migration model, several other popular migration models are also introduced and the corresponding performances are analyzed. For mutation operator, the design of BBO is different from other meta-heuristics. In standard BBO, different candidate solutions have different migration rates and the rate assignment is influential to BBO’s performance. Second, we summarized some popular variants of BBO and related hybrid algorithms that significantly enhance BBO’s performance. This part introduces the development of this algorithm and helps readers understand the way to choose a suitable version of BBO for a given problem. The way to improve algorithms’ performances helps readers design new variants of BBO for specific problems. Third, we present the evaluation of BBO’s performance for both numerical and practical problems. The results demonstrate BBO is competent to solve optimization problems. Despite so many achievements of BBO, some open issues that should be considered and solved in future work in order to make this algorithm more competitive in meta-heuristics.

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

Access this article

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

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    Google Scholar 

  2. Michalewicz Z (1992) Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York

    Book  MATH  Google Scholar 

  3. Kennedy J, Eberhart R, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco, CA

    Google Scholar 

  4. Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inform Sci 291:43–60

    Article  MathSciNet  MATH  Google Scholar 

  5. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge

    MATH  Google Scholar 

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

    Article  Google Scholar 

  7. Wallace A (2005) The geographical distribution of animals (two volumes). Adamant Media Corporation, Boston

    Google Scholar 

  8. Darwin C (2005) The origin of species. Gramercy, New York

    Book  Google Scholar 

  9. MacArthur RH, Wilson EO (1967) The theory of Island biogeography. Princeton University Press, Princeton

    Google Scholar 

  10. Hanski I, Gilpin M (1997) Metapopulation biology. Academic, New York

    MATH  Google Scholar 

  11. Simon D, Ergezer M, Du D (2009) Population distributions in biogeography-based optimization algorithms with elitism. In: IEEE conference on systems, man, and cybernetics, San Antonio, Texas

  12. Simon D, Rarick R, Ergezer M, Du D (2011) Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms. Inform Sci 181(7):1224–1248

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  15. Guo W, Wang L, Wu Q (2016) Numerical comparisons of migration models for multi-objective biogeography-based optimization. Inform Sci 328:302–320. doi:10.1016/j.ins.2015.07.059

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  18. Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12(4):265–319

    Article  MATH  Google Scholar 

  19. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  20. Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inform Sci 192(1):120–142

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  22. Guo W, Wang L, Ge SS, Ren H, Mao Y (2014) Drift analysis of mutation operations for biogeography-based optimization. Soft Comput 19(7):1–12

    MATH  Google Scholar 

  23. Simon D (2011) A probabilistic analysis of a simplified biogeography-based optimization algorithm. Evol Comput 19(2):167–188

    Article  Google Scholar 

  24. Ergezer M, Simon D, Du D (2009) Oppositional biogeography-based optimization. In: 2009 IEEE International conference on systems, man and cybernetics (SMC 2009), vols 1–9, pp 1009–1014, San Antonio, TX

  25. 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, Vienna, pp 695–701

  26. Tizhoosh H (2005) Opposition-based reinforcement learning. J Adv Comput Intell Intell Informatics 10(4):578–585

    Article  Google Scholar 

  27. Ventresca M, Tizhoosh HR (2006) Improving the convergence of backpropagation by opposite transfer functions. BC, Canada, Vancouver, pp 4777–4784

  28. Ventresca M, Tizhoosh HR (2007) Opposite transfer functions and backpropagation through time. HI, United states, Honolulu, pp 570–577

  29. Tizhoosh HR (2009) Opposite fuzzy sets with applications in image processing, Lisbon, Portugal, pp 36 – 41

  30. Rahnamayan RS, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79

    Article  Google Scholar 

  31. Rahnamayan S, Wang GG (2008) Solving large scale optimization problems by opposition-based differential evolution (ode). WSEAS Trans Comput 7(10):1792–1804

    Google Scholar 

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

    Article  Google Scholar 

  33. Feng Q, Liu S, Wu Q, Tang G, Zhang H, Chen H (2013) Modified biogeography-based optimization with local search mechanism. J Appl Math 2013:1–24. doi:10.1155/2013/960524

  34. Roy PK, Mandal D (2012) Quasi-oppositional biogeography-based optimization for multi-objective optimal power flow. Electric Power Compon Syst 40(2):236–256

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  36. Feller W (1971) An introduction to probability theory and its applications, vol 2, 3rd edn. Wiley, New York

    MATH  Google Scholar 

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

    Article  Google Scholar 

  38. Xiong G, Li Y, Chen J, Shi D, Duan X (2014) Polyphyletic migration operator and orthogonal learning aided biogeography-based optimization for dynamic economic dispatch with valve-point effects. Engergy Convers Manag 80:457–468

    Article  Google Scholar 

  39. Feng Q, Liu S, Tang G, Yong L, Zhang J (2013) Biogeography-based optimization with orthogonal crossover. Math Problem Eng 2013(353969):1–20

    Google Scholar 

  40. Zhan Z-H, Zhang J, Li Y, hui Shi Y (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847

    Article  Google Scholar 

  41. Leung Y-W, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5(1):41–53

    Article  Google Scholar 

  42. Ma H, Simon D, Fei M, Xie Z (2013) Variations of biogeography-based optimization and Markov analysis. Inform Sci 220:492–506

    Article  Google Scholar 

  43. Tan L-x, Guo L (2009) Quantum and biogeography based optimization for a class of combinatorial optimization, 2009, pp 969–972, World summit on genetic and evolutionary computation (GEC 09), Shanghai, Peoples Republic China, JUN 12–14

  44. Costa e Silva MdA, Coelho LdS, Lebensztajn L (2012) Multiobjective biogeography-based optimization based on predator-prey approach. IEEE Trans Magn 48(2):951–954

    Article  Google Scholar 

  45. Christy AA, Raj PADV (2014) Adaptive biogeography based predator-prey optimization technique for optimal power flow. Int J Electrical Power Energy Syst 62:344–352

    Article  Google Scholar 

  46. Zhu W, Duan H (2014) Chaotic predator-prey biogeography-based optimization approach for UCAV path planning. Aerospace Sci Techonol 32(1):153–161

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  48. Lohokare MR, Devi S, Pattnaik SS, Panigrahi BK, Joshi JG (2011) Modified biogeography-based optimisation (MBBO). Int J Bio-Inspired Comput 3(4):252–266

    Article  Google Scholar 

  49. Niu Q, Zhang L, Li K (2014) A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells. Energy Convers Manag 86:1173–1185

    Article  Google Scholar 

  50. Gong W, Cai Z, Ling CX (2011) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15(4):645–665

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  52. Boussaid I, Chatterjee A, Siarry P, Ahmed-Nacer M (2011) Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks. IEEE Trans Veh Technol 60(5):2347–2353

    Article  Google Scholar 

  53. Savsani P, Jhala RL, Savsani V (2014) Effect of hybridizing biogeography-based optimization (BBO) technique with artificial immune algorithm (AIA) and ant colony optimization (ACO). Appl Soft Comput 21:542–553

    Article  Google Scholar 

  54. Li X, Yin M (2013) Hybrid artificial bee colony and biogeography based optimization for global numerical optimization. J Comput Theor Nanosci 10(5):1156–1163

    Article  Google Scholar 

  55. Guo W, Li W, Zhang Q, Wang L, Wu Q, Ren H (2014) Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems. Eng Optim 46(11):1465–1484

    Article  MathSciNet  Google Scholar 

  56. Ergezer M, Simon D (2011) Oppositional biogeography-based optimization for combinatorial problems, In: 2011 IEEE congress in evolutionary computation (CEC), New Orleans, LA, June, pp 1496–1503

  57. Mo H, Xu L (2010) Biogeography migration algorithm for traveling salesman problem, vol 1. Springer, Berlin

    MATH  Google Scholar 

  58. Zhao B, Deng C, Yang Y, Peng H (2012) Novel binary biogeography-based optimization algorithm for the Knapsack problem. In: Tan Y, Shi Y, Ji Z (eds) Advances in swarm intelligence, ICSI 2012, PT I, Vol. 7331 of lecture notes in computer science, 2012, pp 217–224, 3rd international conference on swarm intelligence (ICSI), Shenzhen, Peoples Republic China, JUN 17–20

  59. Sharaqa A, Dib N (2014) Design of linear and elliptical antenna arrays using biogeography based optimization. Arabian J Sci Eng 39(4):2929–2939

    Article  Google Scholar 

  60. Laseetha TSJ, Sukanesh R (2014) Certain investigations on the reduction of side lobe level of an uniform linear antenna array using biogeography based optimization technique with sinusoidal migration model and simplified-BBO. Sadhana-Academy Proc Eng Sci 39(1):81–95

    Google Scholar 

  61. Singh U, Kamal TS (2012) Synthesis of thinned planar concentric circular antenna arrays using biogeography-based optimisation. IET Microw Antennas Propag 6(7):822–829

    Article  MathSciNet  Google Scholar 

  62. Ma H, Fei M, Yang Z, Wang H (2014) Wireless networked learning control system based on Kalman filter and biogeography-based optimization method. Trans Inst Meas Control 36(2):224–236

    Article  Google Scholar 

  63. Kaur K, Rattan M, Patterh MS (2014) Biogeography-based optimisation of cognitive radio system. Int J Electr 101(1):24–36

    Article  Google Scholar 

  64. Zhu W (2010) Parallel biogeography-based optimization with GPU acceleration for nonlinear optimization. In: Proceedings of the ASME international design engineering technical conferences and computers and information in engineering conference 2010, vol 1, PTS A AND B, 2010, pp 315–323, ASME international design engineering technical conferences/computers and information in engineering Conference, Montreal, CANADA, AUG 15-18

  65. Mobini MH, Entezari-Maleki R, Movaghar A (2012) Biogeography-based optimization of makespan and reliability in grid computing systems, In: IV International congress on ultra modern telecommunications and control systems 2012 (ICUMT), pp 336–342

  66. Hadidi A, Nazari A (2013) Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm. Appl Therm Eng 51(1–2):1263–1272

    Article  Google Scholar 

  67. Wang X, Duan H, Luo D (2013) Cauchy biogeography-based optimization based on lateral inhibition for image matching. Optik 124(22):5447–5453

    Article  Google Scholar 

  68. Zhang P, Wei P, Yu HY (2012) Biogeography-based optimisation search algorithm for block matching motion estimation. IET Image Process 6(7):1014–1023

    Article  MathSciNet  Google Scholar 

  69. Panchal VK, Singh P, Kaur N, Kundra H (2009) Biogeography based satellite image classification. Int J Comput Sci Inform Sec 6(2):269–274

    Google Scholar 

  70. Johal NK, Singh S, Kundra H (2010) A hybrid fpab/bbo algorithm for satellite image classification. Int J Comput Appl 6(5):31–36

    Google Scholar 

  71. Rahmati SHA, Zandieh M (2012) A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. Int J Adv Manuf Technol 58(9–12):1115–1129

    Article  Google Scholar 

  72. Rashid A, Kim BS, Khambampati AK, Kim S, Kim KY (2010) An oppositional biogeography-based optimization technique to reconstruct organ boundaries in the human thorax using electrical impedance tomography. Physiological measurement 32 (7, SI), 14th international conference on electrical bioimpedance/11th international conference on biomedical applications of electrical impedance tomography, University Florida, Gainesville, FL, APR 04–08

  73. Chatterjee A, Siarry P, Nakib A, Blanc R (2012) An improved biogeography based optimization approach for segmentation of human head ct-scan images employing fuzzy entropy. Eng Appl Artif Intell 25(8):1698–1709

    Article  Google Scholar 

  74. Kalaivani R, Lakshmi P (2014) Biogeography-based optimization of PID tuning parameters for the vibration control of active suspension system. Control Eng Appl Inform 16(1):31–39

    Google Scholar 

  75. Sayed MM, Saad MS, Emara HM, Abou El-Zahab EE (2012) A Novel method for PID tuning using a modified biogeography-based optimization algorithm. In: Proceedings of the 2012 24TH Chinese control and decision conference (CCDC), Chinese control and decision conference, 2012, pp 1642–1647, Taiyuan, Peoples Republic China, May 23–25

  76. Lin J (2014) Parameter estimation for time-delay chaotic systems by hybrid biogeography-based optimization. Nonlinear Dyn 77(3):983–992

    Article  Google Scholar 

  77. Wang J, Li X, Su Z (2013) An improved biogeography based optimization for parameter estimation of chaotic systems. J Comput Theor Nanosci 10(9):2192–2200

    Article  Google Scholar 

  78. Yang G-P, Liu S-Y, Zhang J-K, Feng Q-X (2013) Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm. Appl Intell 39(1):132–143

    Article  Google Scholar 

  79. Wang L, Xu Y (2011) An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems. Expert Syst Appl 38(12):15103–15109

    Article  MathSciNet  Google Scholar 

  80. Roy PK, Ghoshal SP, Thakur SS (2010) Multi-objective optimal power flow using biogeography-based optimization. Electic Power Compon Syst 38(12):1406–1426

    Article  Google Scholar 

  81. Rarick R, Simon D, Villaseca FE, Vyakaranam B (2009) Biogeography-based optimization and the solution of the power flow problem. In: 2009 IEEE international conference on systems, man and cybernetics (SMC 2009), vols 1–9, IEEE international conference on systems man and cybernetics conference proceedings, 2009, pp 1003–1008, San Antonio, TX, OCT 11–14

  82. Bhattacharya A, Chattopadhyay P (2010) Solving complex economic load dispatch problems using biogeography-based optimization. Expert Syst Appl 37(5):3605–3615

    Article  Google Scholar 

  83. Bhattacharya A, Chattopadhyay P (2010) Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst 25(2):1064–1077

    Article  Google Scholar 

  84. Bhattacharya A, Chattopadhyay P (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 

  85. Xiong G, Shi D, Duan X (2013) Multi-strategy ensemble biogeography-based optimization for economic dispatch problems. Appl Engergy 111:801–811

    Article  Google Scholar 

  86. Bansal AK, Kumar R, Gupta RA (2013) Economic analysis and power management of a small autonomous hybrid power system (SAHPS) using biogeography based optimization (BBO) algorithm. IEEE Trans Smart Grid 4(1):638–648

    Article  Google Scholar 

  87. Dong F, Liu D, Wu J, Cen B, Wang H, Song C, Ke L (2014) Design of SVC controller based on improved biogeography-based optimization algorithm. J Appl Math 2014:1–8. doi:10.1155/2014/939326

  88. Jamuna K, Swarup KS (2012) Multi-objective biogeography based optimization for optimal PMU placement. Appl Soft Comput 12(5):1503–1510

    Article  Google Scholar 

  89. Roy PK, Ghoshal SP, Thakur SS (2012) Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization. Int J Electr Power Syst 43(1):830–838

    Article  Google Scholar 

  90. Annamalai S, Govinthasamy R (2013) Biogeography-based optimization for voltage stability improvement and reactive reserve management. Turkish J Electr Eng Comput Sci 21(4):913–923

    Google Scholar 

  91. Thomas G, Lozovyy P, Simon D (2011) Fuzzy robot controller tuning with biogeography-based optimization, vol 2. Springer, Berlin

    Google Scholar 

  92. Du D, Simon D (2013) Complex system optimization using biogeography-based optimization, mathematical problems in engineering (456232). doi:10.1155/2013/456232

  93. Zheng Y-J, Ling H-F, Shi H-H, Chen H-S, Chen S-Y (2014) Emergency railway wagon scheduling by hybrid biogeography-based optimization. Comput Oper Res 43:1–8

    Article  MathSciNet  MATH  Google Scholar 

  94. Huang N, Liu G, He B (2012) Path planning based on voronoi diagram and biogeography-based optimization. In: Tan Y, Shi Y, Ji Z (eds) Advances in swarm intelligence, ICSI 2012, PT I, Vol 7331 of lecture notes in computer science, 2012, pp 225–232, 3rd international conference on swarm intelligence (ICSI), Shenzhen, Peoples Republic China, JUN 17–20

Download references

Acknowledgments

This work is sponsored by the National Natural Science Foundation of China under Grant Nos. 61503287, No. 71371142 and No. 61203250, Program for Young Excellent Talents in Tongji University (2014KJ046), Program for New Century Excellent Talents in University of Ministry of Education of China, Ph.D. Programs Foundation of Ministry of Education of China (20100072110038).

Author information

Authors and Affiliations

  1. Sino-German College of Applied Sciences, Tongji University, Shanghai, 201804, China

    Weian Guo, Ming Chen & Yanfen Mao

  2. Department of Electronics and Information, Tongji University, Shanghai, 201804, China

    Lei Wang & Qidi Wu

Authors
  1. Weian Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanfen Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qidi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, W., Chen, M., Wang, L. et al. A survey of biogeography-based optimization. Neural Comput & Applic 28, 1909–1926 (2017). https://doi.org/10.1007/s00521-016-2179-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2179-x

Keywords