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imBBO: An Improved Biogeography-Based Optimization Algorithm

  • Kai Shi
  • Huiqun YuEmail author
  • Guisheng FanEmail author
  • Xingguang Yang
  • Zheng Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

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.

Keywords

Hybrid migration Biogeography-based Optimization Global optimization 

Notes

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.

References

  1. 1.
    Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)Google Scholar
  2. 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. 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. 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. 5.
    Černý, V.: Thermodynamical approach to the traveling salesman problem: AN efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Deep, K., Thakur, M.: A new mutation operator for real coded genetic algorithms. Appl. Math. Comput. 193(1), 211–230 (2007)MathSciNetzbMATHGoogle Scholar
  7. 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. 8.
    Ekta, M.K.: Biogeography based optimization: a review. In: International Conference on Computing for Sustainable Global Development (2015)Google Scholar
  9. 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. 10.
    Engelbrecht, A.P.: Computational Intelligence - An Introduction, 2nd edn. Wiley, Hoboken (2007)Google Scholar
  11. 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. 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. 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. 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. 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)MathSciNetzbMATHGoogle Scholar
  16. 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. 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. 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_77zbMATHGoogle Scholar
  19. 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)MathSciNetzbMATHGoogle Scholar
  20. 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)MathSciNetzbMATHGoogle Scholar
  21. 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. 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. 23.
    Ma, H.: An analysis of the equilibrium of migration models for biogeography-based optimization. Inf. Sci. 180(18), 3444–3464 (2010)zbMATHGoogle Scholar
  24. 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. 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. 26.
    Ma, H., Simon, D.: Blended biogeography-based optimization for constrained optimization. Eng. Appl. AI 24(3), 517–525 (2011)Google Scholar
  27. 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. 28.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)Google Scholar
  29. 29.
    O’Reilly, U.: Genetic programming II: automatic discovery of reusable programs. Artif. Life 1(4), 439–441 (1994)Google Scholar
  30. 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. 31.
    Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2006).  https://doi.org/10.1007/3-540-31306-0zbMATHGoogle Scholar
  32. 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. 33.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)zbMATHGoogle Scholar
  34. 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. 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. 36.
    Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)Google Scholar
  37. 37.
    Simon, D.: A dynamic system model of biogeography-based optimization. Appl. Soft Comput. 11(8), 5652–5661 (2011)Google Scholar
  38. 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)MathSciNetGoogle Scholar
  39. 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. 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)MathSciNetzbMATHGoogle Scholar
  41. 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)zbMATHGoogle Scholar
  42. 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/BFb0014808Google Scholar
  43. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringEast China University of Science and TechnologyShanghaiChina
  2. 2.Shanghai Key Laboratory of Computer Software Evaluating and TestingShanghaiChina
  3. 3.The Third Research Institute of the Ministry of Public SecurityShanghaiChina

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