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A Comprehensive Review on Bacteria Foraging Optimization Technique

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Multi-objective Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 592))

Abstract

Intelligent applications using evolutionary algorithms are becoming famous because of their ability to handle any real time complex and uncertain situations. Swarm intelligence, now-a-days has become a research focus which studies the collective behavior existing among the natural species which lives in group. Bacteria Foraging Optimization (BFO) is an optimization algorithm based on the social intelligence behavior of E.coli bacteria. Literature has witnessed the applications of BFO algorithm and the results reported are promising with regard to its convergence and accuracy. Several studies based on distributed control and optimization also suggested that algorithm based on BFO can be treated as global optimization technique. In this chapter, we have focused on the behavior of biological bacterial colony followed by the optimization algorithm based on bacterial colony foraging. We have also explored variations in the components of BFO algorithm (Revised BFO), hybridization of BFO with other Evolutionary Algorithms (Hybrid BFO) and multi-objective BFO. Finally, we have analyzed some applications of BFO algorithm in various domains.

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References

  1. Lei, W., Qi, K., Qi-di, W.: Nature-inspired computation effective realization of artificial intelligence. SETP 27(5), 126–34 (2007)

    Google Scholar 

  2. de Castro, L.N.: Fundamentals of natural computing: an overview. Phys. Life Rev. 4, 1–36 (2007)

    Article  MathSciNet  Google Scholar 

  3. Zang, H., Zhang, S., Hapeshi, K.: A review of nature-inspired algorithms. J. Bionic Eng. 7(Suppl.), S232–7 (2010)

    Article  Google Scholar 

  4. Schut, M.C.: On model design for simulation of collective intelligence. Inf. Sci. 180, 132–55 (2010)

    Article  Google Scholar 

  5. Dressler, F., Akan, O.B.: A survey on bio-inspired networking. Comput. Netw. 54, 81–900 (2010)

    Article  Google Scholar 

  6. Agrawal, V., Sharma, H., Bansal, J.C.: Bacteria foraging optimization: a survey. In: Proceedings of International Conference on SocProS 2011. AISC130, pp. 227–242 (2012)

    Google Scholar 

  7. El-Abd, M.: Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf. Sci. 182, 243–3 (2012)

    Article  MathSciNet  Google Scholar 

  8. Brownlee, J.: Clever algorithms nature inspired programming recipes (2012)

    Google Scholar 

  9. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 5(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  10. Liebes, S.: A Walk Through Time: From Stardust to Us. Wiley (1998)

    Google Scholar 

  11. Margulies, L., Dolan, M.F.: Early Life: Evolution on the Precambrian Earth. Jones and Bartlett (2002)

    Google Scholar 

  12. Rajni, Chana, I.: Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. Future Gener. Comput. Syst. 29(3), 751–762 (2013)

    Google Scholar 

  13. Pedregal, P.: Introduction to Optimization. Springer International Edition (2004)

    Google Scholar 

  14. Terashima, H., Kojima, S., Homma, M.: Flagellar motility in bacteria: structure and function of flagellar motor. In: International Review of Cell and Molecular Biology, vol. 270 (2008)

    Google Scholar 

  15. Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications, pp. 23–55. Springer, Berlin (2009)

    Google Scholar 

  16. Chen, H., Zhu, Y., Hu, K.: Cooperative bacterial foraging optimization. Discret. Dyn. Nat. Soc. 2009, 17 (2009)

    MathSciNet  Google Scholar 

  17. Dasgupta, S., Das, S., Abraham, A., Biswas, A.: Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans. Evolut. Comput. 13(4), 919–41 (2009)

    Article  Google Scholar 

  18. Tripathy, M., Mishra, S., Lai, L.L., Zhang, Q.P.: Transmission loss reduction based on FACTS and bacteria foraging algorithm. In: Proceedings of PPSN, pp. 222–231 (2006)

    Google Scholar 

  19. Li, M.S., Tang, W.J., Tang, W.H., Wu, Q.H., Saunders, J.R.: Bacteria foraging algorithm with varying population for optimal power flow. In: Proceedings of EvoWorkshops 2007. LNCS, vol. 4448, pp. 32–41 (2007)

    Google Scholar 

  20. Chen, H., Zhu, Y., Hu, K.: Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning. Appl. Soft Comput. 10, 539–47 (2010)

    Article  Google Scholar 

  21. Dasgupta, S., Biswas, A., Das, S., Panigrahi, B.K., Abraham, A.: A Micro-Bacterial Foraging Algorithm for High-Dimensional Optimization (2009)

    Google Scholar 

  22. Biswas, A., Dasgupta, S., Das, S., Abraham, A.: Synergy of PSO and bacterial foraging optimization: a comparative study on numerical benchmarks. In: Proceedings 2nd International Symposium Hybrid Artificial Intelligent Systems (HAIS). Advances Soft Computing Series, Innovations in Hybrid Intelligent Systems. ASC, vol. 44, pp. 255–263. Springer, Germany (2007)

    Google Scholar 

  23. Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf. Sci. 177, 3918–37 (2007)

    Article  Google Scholar 

  24. Kim, D.H.: Hybrid GA-BF based intelligent PID controller tuning for AVR system. Appl. Soft Comput. 11, 11–22 (2011)

    Article  Google Scholar 

  25. Okaeme, N.A., Zanchetta, P.: Hybrid bacterial foraging optimization strategy for automated experimental control design in electrical drives. IEEE Trans. Ind. Inf. 9, 668–8 (2013)

    Article  Google Scholar 

  26. Dasgupta, S., Biswas, A., Das, S., Abraham, A.: Automatic circle detection on images with an adaptive bacterial foraging algorithm. In: GECCO’08, Atlanta, 12–16 July 2008

    Google Scholar 

  27. Korani, W.: Bacterial foraging oriented by particle swarm optimization strategy for PID tuning. In: GECCO’08 Proceedings of the Genetic and Evolutionary Computation Conference. ACM, pp. 1823–1826. Atlanta (2008)

    Google Scholar 

  28. Gollapudi, S.V.R.S., Pattnaika, S.S., Bajpaib, O.P., Devi, S., Bakwad, K.M.: Velocity modulated bacterial foraging optimization technique (VMBFO). Appl. Soft Comput. 11, 154–65 (2011)

    Article  Google Scholar 

  29. Abd-Elazim, S.M., Ali, E.S.: A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Electr. Power Energy Syst. 46, 334–41 (2013)

    Article  Google Scholar 

  30. Abraham, A., Guo, H., Liu, H.: Swarm intelligence: foundations, perspectives and applications. Stud. Comput. Intell. (SCI) 26, 3–25 (2006)

    Article  Google Scholar 

  31. Deb, K.: Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  32. Caramia, M., Dell Olmo, P.: Multi-objective Management in Freight Logistics Increasing Capacity, Service Level and Safety with Optimization Algorithms, Springer, London (2008). ISBN: 978-1-84800-381-1

    Google Scholar 

  33. Guzman, M.A., Delgado, A., De Carvalho, J.: A novel multiobjective optimization algorithm based on bacterial chemotaxis. Eng. Appl. Artif. Intell. 23(3), 292–301 (2010)

    Article  Google Scholar 

  34. Panigrahi, B.K., Pandi, V.R., Sharma, R., Das, S., Das, S.: Multiobjective bacteria foraging algorithm for electrical load dispatch problem. Energy Convers. Manag. 52, 1334–42 (2011)

    Article  Google Scholar 

  35. Niu, B., Wang, H., Wang, J., Tan, L.: Multi-objective bacterial foraging optimization. Neurocomputing 116, 336–45 (2013)

    Article  Google Scholar 

  36. Daryabeigi, E., Zafari, A., Shamshirband, S., Anuar, N.B., Kiah, M.L.M.: Calculation of optimal induction heater capacitance based on the smart bacterial foraging algorithm. Electr. Power Energy Syst. 61, 326–34 (2014)

    Article  Google Scholar 

  37. Daryabeigi, E., Dehkordi, B.M.: Smart bacterial foraging algorithm based controller for speed control of switched reluctance motor drives. Electr. Power Energy Syst. 62, 364–73 (2014)

    Article  Google Scholar 

  38. Abharian, A.E., Sarabi, S.Z., Yomi, M.: Optimal sigmoid nonlinear stochastic control of HIV-1 infection basedon bacteria foraging optimization method. Biomed. Signal Process. Control 104, 184–91 (2013)

    Google Scholar 

  39. Vivekanandana, K., Ramyachitra, D.: Bacteria foraging optimization for protein sequence analysis on the grid. Future Gener. Comput. Syst. 28, 647–56 (2012)

    Article  Google Scholar 

  40. Niu, B., Fan, Y., Xiao, H., Xue, B.: Bacterial foraging based approaches to portfolio optimization with liquidity risk. Neurocomputing 98, 90–100 (2012)

    Article  Google Scholar 

  41. Sanyal, N., Chatterjee, A., Munshi, S.: An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation. Expert Syst. Appl. 38, 15489–98 (2011)

    Article  Google Scholar 

  42. Verma, O.P., Hanmandlu, M., Kumar, P., Chhabra, S., Jindal, A.: A novel bacterial foraging technique for edge detection. Pattern Recognit. Lett. 32, 1187–96 (2011)

    Article  Google Scholar 

  43. Sathya, P.D., Kayalvizhi, R.: Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images. Measurement 44, 1828–8 (2011)

    Article  Google Scholar 

  44. Panda, R., Naik, M.K., Panigrahi, B.K.: Face recognition using bacterial foraging strategy. Swarm Evol. Comput. 1, 138–46 (2011)

    Article  Google Scholar 

  45. Verma, O.P., Sharmab, R., Kumar, D.: Binarization based image edge detection using bacterial foraging algorithm. Procedia Technol. 6, 315–23 (2012)

    Article  Google Scholar 

  46. Bhushan, B., Singh, M.: Adaptive control of DC motor using bacterial foraging algorithm. Appl. Soft Comput. 11, 4913–20 (2011)

    Article  Google Scholar 

  47. Venkaiah, Ch., Vinod Kumar, D.M.: Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators. Appl. Soft Comput. 11, 4921–30 (2011)

    Article  Google Scholar 

  48. Ali, E.S., Abd-Elazim, S.M.: TCSC damping controller design based on bacteria foraging optimization algorithm for a multimachine power system. Electr. Power Energy Syst. 37, 23–30 (2012)

    Article  Google Scholar 

  49. Vaisakh, K., Praveena, P., Rama Mohana Rao, S., Meah, K.: Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm. Electr. Power Energy Syst. 39(1), 56–67 (2012)

    Article  Google Scholar 

  50. Abd-Elazim, S.M., Ali, E.S.: Coordinated design of PSSs and SVC via bacteria foraging optimization algorithm in a multimachine power system. Electr. Power Energy Syst. 41, 44–53 (2012)

    Article  Google Scholar 

  51. Abd-Elazim, S.M., Ali, E.S.: Bacteria foraging optimization algorithm based SVC damping controller design for power system stability enhancement. Electr. Power Energy Syst. 43, 933–40 (2012)

    Article  Google Scholar 

  52. Rajinikant, V., Latha, K.: I-PD controller tuning for unstable system using bacterial foraging algorithm: a study based on various error criterion. Appl. Comput. Intell. Soft Comput. 2012, Article ID 329389 (2012)

    Google Scholar 

  53. Saikia, L.C., Sinha, N., Nanda, J.: Maiden application of bacterial foraging based fuzzy IDD controller in AGC of a multi-area hydrothermal system. Electr. Power Energy Syst. 45(1), 98–106 (2013)

    Article  Google Scholar 

  54. Azizipanah-Abarghooee, R.: A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch. Electr. Power Energy Syst. 49, 414–429 (2013)

    Article  Google Scholar 

  55. Mohamed Imran, A., Kowsalya, M.: Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evol. Comput. 15, 58–65 (2013)

    Article  Google Scholar 

  56. Santos, V.S., Felipe, P.V., Sarduy, J.G.: Bacterial foraging algorithm application for induction motor field efficiency estimation under unbalanced voltages. Measurement 46, 2232–7 (2013)

    Article  Google Scholar 

  57. Devi, S., Geethanjali, M.: Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation. Expert Syst. Appl. 41, 2772–81 (2014)

    Article  Google Scholar 

  58. Nouria, H., Hong, T.S.: A bacteria foraging algorithm based cell formation considering operation time. J. Manuf. Syst. 31, 326–6 (2012)

    Article  Google Scholar 

  59. Nouria, H., Hong, T.S.: Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations. J. Manuf. Syst. 32, 20–31 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, 632014, India

    B. Selva Rani & Ch. Aswani Kumar

Authors
  1. B. Selva Rani
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  2. Ch. Aswani Kumar
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Corresponding author

Correspondence to Ch. Aswani Kumar .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  2. Department of Computer Science and Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, India

    Alok Kumar Jagadev

  3. Gandhi Institute for Technological Advancement, Bhubaneswar, India

    Mrutyunjaya Panda

Appendix

Appendix

(See Table 4)

Table 4 Nomenclature
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Selva Rani, B., Aswani Kumar, C. (2015). A Comprehensive Review on Bacteria Foraging Optimization Technique. In: Dehuri, S., Jagadev, A., Panda, M. (eds) Multi-objective Swarm Intelligence. Studies in Computational Intelligence, vol 592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46309-3_1

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  • DOI: https://doi.org/10.1007/978-3-662-46309-3_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46308-6

  • Online ISBN: 978-3-662-46309-3

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