Biogeography-Based Optimization

This web site is dedicated to biogeography-based optimization (BBO) and related material. If you have any papers or software that you would like to be included on this web page, please email the information to Dan Simon at d-dot-j-dot-simon-at-csuohio-dot-edu.


The original BBO paper can be downloaded from http://academic.csuohio.edu/simond/bbo. Note that the Matlab software on that web site is inefficient; you can download better software from the web site below.


MATLAB® software is available for continuous BBO. The code is designed so that it can be easily modified and customized for specific optimization problems by users who are familiar with MATLAB m-files. A tutorial article, including pseudo-code and simple Matlab code, is also available at https://en.wikipedia.org/wiki/Biogeography_based_optimization.


Sarvesh Nikumbh has written R software for continuous BBO.


Hybrid Invasive Weed / Biogeography-Based Optimization


An Adobe Flex graphical user interface is available for genetic algorithms and BBO:


BBO Papers:

I used to list all of the BBO papers that I knew about on this web page. However, it has become too hard to keep up with all of the papers that are published. If you want a list of BBO papers, it is easy to search on Google Scholar. I will continue to make BBO papers available for download on this web page, so feel free to email me if you have any BBO papers that you would like me to include here.

Journal Papers:

  1. A. R. Al-Roomi and M. E. El-Hawary, Metropolis Biogeography-Based Optimization, Information Sciences, vol. 360, pp. 73-95, 2016
  2. F. A. Albasri, A. R. Alroomi, and J. H. Talaq, Optimal Coordination of Directional Overcurrent Relays Using Biogeography-Based Optimization Algorithms, IEEE Transactions on Power Delivery, vol. 30, no. 4, pp. 1810-1820, 2015
  3. W. L. Lim, M. A. S. Alias, and H. Haron, A Hybrid Metaheuristic for the Generalized Quadratic Assignment Problem, 2015 IEEE Student Conference on Research and Development (SCOReD), Kuala Lumpur, Malaysia, pp. 467-471, December 2015
  4. W. L. Lim, A. Wibowo, M. I. Desa, and H. Haron, A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem, Computational Intelligence and Neuroscience, vol. 2016, Article ID 5803893, 12 pages, 2016
  5. H. Ma, D. Simon, and M. Fei, On the Convergence of Biogeography-Based Optimization for Binary Problems, Mathematical Problems in Engineering, vol. 2014, Article ID 147457, 11 pages, 2014
  6. D. Simon, M. Omran, and M. Clerc, Linearized Biogeography-Based Optimization with Re-initialization and Local Search, Information Sciences, vol. 267, pp. 140-157, May 2014
  7. H. Ma, D. Simon, M. Fei, X. Shu, and Z. Chen, Hybrid Biogeography-Based Evolutionary Algorithms, Engineering Applications of Artificial Intelligence, vol. 30, pp. 213-224, April 2014
  8. A. R. Alroomi, F. A. Albasri, and J. H. Talaq, Solving the Associated Weakness of Biogeography-Based Optimization Algorithm, International Journal on Soft Computing, vol. 4, no. 4, pp. 1-20, 2013
  9. A. R. Alroomi, F. A. Albasri, and J. H. Talaq, A Comprehensive Comparison of the Original Forms of Biogeography-Based Optimization Algorithms, International Journal on Soft Computing, Artificial Intelligence and Applications, vol. 2, no. 5/6, pp. 11-30, 2013
  10. D. Du and D. Simon, Complex System Optimization Using Biogeography-Based Optimization, Mathematical Problems in Engineering, vol. 2013, Article ID 456232, 18 pages, 2013
  11. H. Ma, D. Simon, M. Fei, and Z. Chen, On the equivalences and differences of evolutionary algorithms, Engineering Applications of Artificial Intelligence, vol. 26, no. 10, pp. 2397-2407, 2013
  12. S. Rahmati and M. Zandieh, A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem, International Journal of Advanced Manufacturing Technology, vol. 58, pp. 1115-1129, 2013
  13. W. Guo, L. Wang, C. Si, Y. Zhang, H. Tian, and J. Hu, Novel Migration Operators of Biogeography-based Optimization and Markov Analysis, submitted for publication, 2015
  14. W. Guo, L. Wang, S. Ge, H. Ren, and Y. Mao, Drift Analysis of Mutation for Biogeography-Based Optimization, Soft Computing, vol. 19, pp. 1881-1892, 2015
  15. W. Guo, W. Li, Q. Zhang, L. Wang, Q. Wu, and H. Ren Biogeography-Based Particle Swarm Optimization with Fuzzy Elitism and Its Applications to Constrained Engineering Problems, Engineering Optimization, vol. 46, no. 11, pp. 1465-1484, 2014
  16. W. Guo, L. Wang, and Q. Wu, An analysis of the migration rates for biogeography-based optimization, Information Sciences, vol. 254, pp. 111-140, 2014
  17. H. Ma, D. Simon, M. Fei, and Z. Xie, Variations of biogeography-based optimization and Markov analysis, Information Sciences, vol. 220, pp. 492-506, January 2013
  18. H. Ma and M. Fei, Integrated evolutionary optimized methods for global optimization of the bifunctional catalyst problem, EMBnet.journal, vol. 18, November 2012
  19. D. Simon, A dynamic system model of biogeography-based optimization, Applied Soft Computing, vol. 11, no. 8, pp. 5652-5661, December 2011
  20. H. Ma and D. Simon, Analysis of migration models of biogeography-based optimization using Markov theory, Engineering Applications of Artificial Intelligence, vol. 24, no. 6, pp. 1052-1060, September 2011
  21. B. Igelnik and D. Simon, The eigenvalues of a tridiagonal matrix in biogeography, Applied Mathematics and Computing, vol. 218, no. 1, pp. 195-201, September 2011
  22. D. Simon, A probabilistic analysis of a simplified biogeography-based optimization algorithm, Evolutionary Computation, vol. 19, no. 2, pp. 167-188, Summer 2011
  23. W. Gong, Z. Cai, and C. Ling, DE/BBO: A hybrid differential evolution with biogeography-based optimization for global numerical optimization, Soft Computing, vol. 15, no. 4, pp. 645-665, April 2011
  24. H. Ma and D. Simon, Blended biogeography-based optimization for constrained optimization, Engineering Applications of Artificial Intelligence, vol. 24, no. 3, pp. 517-525, April 2011
  25. D. Simon, R. Rarick, M. Ergezer, and D. Du, Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms, Information Sciences, vol. 181, no. 7, pp. 1224-1248, April 2011
  26. A. Sinha, S. Das, and B. Panigrahi, A linear state-space analysis of the migration model in an island biogeography system, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 41, no. 2, pp. 331-337, March 2011
  27. D. Simon, M. Ergezer, D. Du, and R. Rarick, Markov models for biogeography-based optimization, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 41, no. 1, pp. 299-306, January 2011
  28. H. Kundra and M. Sood, Cross-country path finding using hybrid approach of PSO and BBO, International Journal of Computer Applications, vol. 7, no. 6, pp. 15-19, September 2010
  29. H. Ma, An analysis of the equilibrium of migration models for biogeography-based optimization, Information Sciences, vol. 180, no. 18, pp. 3444-3464, 15 September 2010
  30. W. Gong, Z. Cai, C. Ling, and H. Li, A real-coded biogeography-based optimization with mutation, Applied Mathematics and Computation, vol. 216, no. 9, pp. 2749-2758, July 2010
  31. S. Kumar, P. Bhalla, and A. Singh, Fuzzy rule base generation from numerical data using biogeography-based optimization, Institution of Engineers Journal of Electronics and Telecomm Engineering, vol. 90, pp. 8-13, July 2009
  32. D. Simon, Biogeography-based optimization, IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702-713, December 2008

Book Chapters:

  1. A. R. Al-Roomi and M. E. El-Hawary, Economic Load Dispatch Using Hybrid MpBBO-SQP Algorithm, in: Nature-Inspired Computation in Engineering (X.-S. Yang, editor) Springer International Publishing, pp. 217-250, 2016
  2. G. Thomas, T. Wilmot, S. Szatmary, D. Simon, and W. Smith, Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control, in: Efficiency and Scalability Methods for Computational Intellect (B. Igelnik and J. Zurada, editors) IGI Global, pp. 142-161, 2013
  3. D. Du and D. Simon, Biogeography-Based Optimization for Large Scale Combinatorial Problems, in: Efficiency and Scalability Methods for Computational Intellect (B. Igelnik and J. Zurada, editors) IGI Global, pp. 197-217, 2013
  4. M. Ovreiu and D. Simon, Cardiomyopathy Detection from Electrocardiogram Features, in: Cardiomyopathies - From Basic Research to Clinical Management (J. Veselka, editor) InTech, pp. 117-134, 2012
  5. P. Lozovyy, G. Thomas, and D. Simon, Biogeography-based optimization for robot controller tuning, in: Computational Modeling and Simulation of Intellect: Current State and Future Perspectives (B. Igelnik, editor) IGI Global, pp. 162-181, 2011

Conference Papers:

  1. A. Alroomi, F. Albasri, and J. Talaq, Performance comparison between the original forms of biogeography-based optimization algorithms, Second International Conference on Advanced Information Technologies and Applications, Dubai, UAE, pp. 121-140, November 2013 - Presentation Slides (20 MB)
  2. A. Alroomi, F. Albasri, and J. Talaq, Essential modifications on biogeography-based optimization algorithm, Second International Conference on Advanced Information Technologies and Applications, Dubai, UAE, pp. 141-160, November 2013 - Presentation Slides (22 MB)
  3. T. Wilmot, G. Thomas, B. Montavon, R. Rarick, A. van den Bogert, S. Szatmary, D. Simon, W. Smith, and S. Samorezov, Biogeography-Based Optimization for Hydraulic Prosthetic Knee Control, Medical Cyber-Physical Systems Workshop, Philadelphia, Pennsylvania, pp. 18-25, April 2013
  4. A. Shah, D. Simon, and H. Richter, Constrained biogeography-based optimization for invariant set computation, American Control Conference, Montreal, Canada, pp. 2639-2644, June 2012
  5. S. Nikumbh, S. Ghosh, and V. Jayaraman, Biogeography-Based Informative Gene Selection and Cancer Classification Using SVM and Random Forests, IEEE World Congress on Computational Intelligence, Brisbane, Australia, pp. 187-192, June 2012
  6. H. Ma, M. Fei, Z. Ding, and J. Jin, Biogeography-Based Optimization with Ensemble of Migration Models for Global Numerical Optimization, IEEE World Congress on Computational Intelligence, Brisbane, Australia, pp. 2981-2988, June 2012
  7. C. Scheidegger, A. Shah, and D. Simon, Distributed Learning with Biogeography-Based Optimization, 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, Syracuse, New York, pp. 203-215, June 2011
  8. G. Thomas, P. Lozovyy, and D. Simon, Fuzzy Robot Controller Tuning with Biogeography-Based Optimization, 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, Syracuse, New York, pp. 319-327, June 2011
  9. M. Ergezer and D. Simon, Oppositional biogeography-based optimization for combinatorial problems, IEEE Congress on Evolutionary Computation, New Orleans, Louisiana, June 2011
  10. S. Bansal, S. Kumar, H. Sharma ,and P. Bhalla, Generation of Golomb ruler sequences and optimization using biogeography based optimization, 5th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology, Haryana, India, February 2011
  11. J. Abell and D. Du, A framework for multiobjective, biogeography-based optimization of complex system families, AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, Fort Worth, Texas, September 2010
  12. S. Kumar, P. Narula, and T. Ahmed, Biogeography Based Optimization Approach for Rule extraction for a Battery Charger System, National Conference on Advances in VLSI, Embedded & Communication, Faridabad, India, August 2010
  13. M. Ovreiu and D. Simon, Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease, Genetic and Evolutionary Computation Conference, Portland, Oregon, pp. 1235-1242, July 2010
  14. H. Ma and D. Simon, Biogeography-based optimization with blended migration for constrained optimization problems, Genetic and Evolutionary Computation Conference, Portland, Oregon, pp. 417-418, July 2010
  15. H. Mo and L. Xu, Biogeography migration algorithm for traveling salesman problem, International Conference on Swarm Intelligence, Beijing, June 2010
  16. S. Kumar, P. Narula, and T. Ahmed, Knowledge extraction from numerical data for the Mamdani type fuzzy systems: A BBO approach, Innovative Practices in Management and Information Technology for Excellence, Jagadhri, India, May 2010
  17. H. Ma, S. Ni, and M. Sun, Equilibrium species counts and migration model tradeoffs for biogeography-based optimization, IEEE Conference on Decision and Control, Shanghai, pp. 3306-3310, December 2009
  18. M. Ergezer, D. Simon, and D. Du, Oppositional biogeography-based optimization, IEEE Conference on Systems, Man, and Cybernetics, San Antonio, Texas, pp. 1035-1040, October 2009
  19. R. Rarick, D. Simon, F. Villaseca, and B. Vyakaranam, Biogeography-based optimization and the solution of the power flow problem, IEEE Conference on Systems, Man, and Cybernetics, San Antonio, Texas, pp. 1029-1034, October 2009
  20. D. Du, D. Simon, and M. Ergezer, Biogeography-based optimization combined with evolutionary strategy and immigration refusal, IEEE Conference on Systems, Man, and Cybernetics, San Antonio, Texas, pp. 1023-1028, October 2009
  21. D. Simon, M. Ergezer, and D. Du, Population distributions in biogeography-based optimization algorithms with elitism, IEEE Conference on Systems, Man, and Cybernetics, San Antonio, Texas, pp. 1017-1022, October 2009
  22. B. Gardner and D. Simon, Evolutionary algorithm sandbox, IEEE Conference on Systems, Man, and Cybernetics, San Antonio, Texas, pp. 583-588, October 2009
  23. H. Kundra, A. Kaur, and V. Panchal, An integrated approach to biogeography based optimization with case based reasoning for retrieving groundwater possibility, 8th Annual Asian Conference and Exhibition on Geospatial Information, Technology and Applications, Singapore, August 2009

This material is based upon work supported by the National Science Foundation under Grant No. 0826124. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Last update May 20, 2016