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Bat Algorithm and Directional Bat Algorithm with Case Studies

  • Asma ChakriEmail author
  • Haroun Ragueb
  • Xin-She Yang
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 744)

Abstract

In recent years, the Bat Algorithm (BA) is becoming a standard optimization tool used by scientists and engineers to solve many problems in different engineering fields. One of the most important characteristics of the bat algorithm is its easy, comprehensible structure which simplifies the computer implementation, in addition to its ability to obtain reliable results for low dimensional problems. As the problem complexity increases, several studies pointed out that premature convergence may occur when the algorithm may get trapped at a local optimum. To overcome this without losing the main BA characteristics (simplicity and reliability), the directional echolocation has been introduced to the mainframe of BA to become what is known as the directional Bat Algorithm (dBA). In this paper, we discuss the main features of the dBA and their contributions in improving the exploitation and exploration capabilities of the standard BA. We also analyze the performance of dBA in optimizing unimodal and multimodal functions in addition to a constrained engineering problem. The results are compared with those obtained by BA and also a new competitive improved BA version, namely the Novel Bat Algorithm (NBA). The ANOVA one way analysis has demonstrated the superiority of the directional bat algorithm.

Keywords

Bat Algorithm Directional bat algorithm Echolocation Optimization Nature-inspired algorithm Swarm intelligence 

References

  1. 1.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995. vol. 1944, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  4. 4.
    Gandomi, A.H., Yang, X.-S., Alavi, A.H., Talatahari, S.: Bat algorithm for constrained optimization tasks. Neural Comput. Appl. 22(6), 1239–1255 (2013)CrossRefGoogle Scholar
  5. 5.
    Bora, T.C., Coelho, L., Lebensztajn, L.: Bat-inspired optimization approach for the brushless DC wheel motor problem. IEEE Trans. Magneti. 48(2), 947–950 (2012)CrossRefGoogle Scholar
  6. 6.
    Bahmani-Firouzi, B., Azizipanah-Abarghooee, R.: Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int. J. Electr. Power Energy Syst. 56, 42–54 (2014)CrossRefGoogle Scholar
  7. 7.
    Ali, E.S.: Optimization of power system stabilizers using BAT search algorithm. Int. J. Electr. Power Energy Syst. 61, 683–690 (2014)CrossRefGoogle Scholar
  8. 8.
    Sambariya, D.K., Prasad, R.: Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm. Int. J. Electri. Power Energy Syst. 61, 229–238 (2014)CrossRefGoogle Scholar
  9. 9.
    Biswal, S., Barisal, A.K., Behera, A., Prakash, T.: Optimal power dispatch using BAT algorithm. In: 2013 International Conference on Energy Efficient Technologies for Sustainability, 10–12 April 2013, pp. 1018–1023 (2013)Google Scholar
  10. 10.
    Wang, G., Guo, L., Duan, H., Liu, L., Wang, H.: A bat algorithm with mutation for UCAV path planning. Sci. World J. 2012, 15 (2012)Google Scholar
  11. 11.
    Khatir, S., Belaidi, I., Serra, R., Abdel Wahab, M., Khatir, T.: Numerical study for single and multiple damage detection and localization in beam-like structures using BAT algorithm. J. VibroEng. 18(1), 202–213 (2016)Google Scholar
  12. 12.
    Kang, M., Kim, J., Kim, J.-M.: Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm. Inf. Sci. 294, 423–438 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zhang, J.W., Wang, G.G.: Image matching using a bat algorithm with mutation. In: Applied Mechanics and Materials. Trans. Tech. Publ, pp. 88–93 (2012)Google Scholar
  14. 14.
    Karri, C., Jena, U.: Fast vector quantization using a Bat algorithm for image compression. Eng. Sci. Technol. Int. J. 19(2), 769–781 (2016)CrossRefGoogle Scholar
  15. 15.
    Marichelvam, M.K., Prabaharan, T., Yang, X.-S., Geetha, M.: Solving hybrid flow shop scheduling problems using bat algorithm. Int. J. Logist. Econo. Globalisation 5(1), 15–29 (2013)CrossRefGoogle Scholar
  16. 16.
    Fister, I., Rauter, S., Yang, X.-S., Ljubič, K., Fister, Jr. I.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149(Part B), 993–1002 (2015)Google Scholar
  17. 17.
    Tsai, P.W., Pan, J.S., Liao, B.Y., Tsai, M.J., Istanda, V.: Bat algorithm inspired algorithm for solving numerical optimization problems. Appl. Mech. Materi. Trans. Tech. Publ., 134–137 (2012)Google Scholar
  18. 18.
    Fister Jr., I., Fister, D., Yang, X.-S.: A hybrid bat algorithm. ElektrotehniˇSki Vestnik 80(1–2), 1–7 (2013)zbMATHGoogle Scholar
  19. 19.
    Fister, I., Fong, S., Brest, J.: A novel hybrid self-adaptive bat algorithm. Sci. World J. 2014, 12 (2014)Google Scholar
  20. 20.
    Cai, X., Wang, L., Kang, Q., Wu, Q.: Bat algorithm with Gaussian walk. Int. J. Bio-Inspired Comput. 6(3), 166–174 (2014)CrossRefGoogle Scholar
  21. 21.
    Gandomi, A.H., Yang, X.-S.: Chaotic bat algorithm. J. Computat. Sci. 5(2), 224–232 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Li, L., Zhou, Y.: A novel complex-valued bat algorithm. Neural Comput. Appl. 25(6), 1369–1381 (2014)CrossRefGoogle Scholar
  23. 23.
    Osaba, E., Yang, X.-S., Diaz, F., Lopez-Garcia, P., Carballedo, R.: An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems. Eng. Appl. Artif. Intell. 48, 59–71 (2016)CrossRefGoogle Scholar
  24. 24.
    Talatahariand, S., Kaveh, A.: Improved bat algorithm for optimum design of large-scale truss structures. Iran Univ. Sci. Technol. 5(2), 241–254 (2015)Google Scholar
  25. 25.
    Chakri, A., Khelif, R., Benouaret, M.: Improved bat algorithm for structural reliability assessment: application and challenges. Multidiscip. Model. Mater. Struct. 12(2), 218–253 (2016)CrossRefGoogle Scholar
  26. 26.
    Kora, P., Kalva, S.R.: Improved Bat algorithm for the detection of myocardial infarction. SpringerPlus 4(1), 666 (2015)CrossRefGoogle Scholar
  27. 27.
    Li, P., Zhou, Z., Shi, R.: Probabilistic optimal operation management of microgrid using point estimate method and improved bat algorithm. In: 2014 IEEE PES General Meeting Conference and Exposition, 27–31 July 2014, pp. 1–5 (2014)Google Scholar
  28. 28.
    Enache, A.C., Sgârciu, V.: Anomaly intrusions detection based on support vector machines with an improved bat algorithm. In: 2015 20th International Conference on Control Systems and Computer Science, 27–29 May 2015, pp. 317–321 (2015)Google Scholar
  29. 29.
    Kavousi-Fard, A., Niknam, T., Fotuhi-Firuzabad, M.: A novel stochastic framework based on cloud theory and θ-modified bat algorithm to solve the distribution feeder reconfiguration. IEEE Trans. Smart Grid 7(2), 740–750 (2016)Google Scholar
  30. 30.
    Pérez, J., Valdez, F., Castillo, O.: A new bat algorithm with fuzzy logic for dynamical parameter adaptation and its applicability to fuzzy control design. In: Castillo, O., Melin, P. (eds.) Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics: Theory and Applications, pp. 65–79. Springer International Publishing, Cham (2015)Google Scholar
  31. 31.
    Chakri, A., Khelif, R., Benouaret, M., Yang, X.-S.: New directional bat algorithm for continuous optimization problems. Expert Syst. Appl. 69, 159–175 (2017)CrossRefGoogle Scholar
  32. 32.
    Chakri, A., Yang, X.-S., Khelif, R., Benouaret, M.: Reliability-based design optimization using the directional bat algorithm. Neural Comput. Appl. (2017)Google Scholar
  33. 33.
    Feoktistov, V.: Differential evolution: in search of solutions, vol 5. Springer Science & Business Media (2007)Google Scholar
  34. 34.
    Iztok, F.J., Fister, D., Fister, I.: Differential evolution strategies with random forest regression in the bat algorithm. In: Paper presented at the Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, Amsterdam, The Netherlands (2013)Google Scholar
  35. 35.
    Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  36. 36.
    Meng, X., Gao, X., Liu, Y.: A novel hybrid bat algorithm with differential evolution strategy for constrained optimization. Int. J. Hybrid Inf. Technol. 8(1), 383–396 (2015)CrossRefGoogle Scholar
  37. 37.
    Xie, J., Zhou, Y., Chen, H.: a novel bat algorithm based on differential operator and lévy flights trajectory. Comput. Intell. Neurosci. 2013, 13 (2013)CrossRefGoogle Scholar
  38. 38.
    X-s, H., Ding, W.-J., Yang, X.-S.: Bat algorithm based on simulated annealing and Gaussian perturbations. Neural Comput. Appl. 25(2), 459–468 (2014)CrossRefGoogle Scholar
  39. 39.
    Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Statist. Sci. 8(1), 10–15 (1993)CrossRefzbMATHGoogle Scholar
  40. 40.
    Wang, G., Guo, L.: A novel hybrid bat algorithm with harmony search for global numerical optimization. J. Appl. Math. 2013, 21 (2013)MathSciNetzbMATHGoogle Scholar
  41. 41.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  42. 42.
    Nguyen, T.-T., Pan, J.-S., Dao, T.-K., Kuo, M.-Y., Horng, M.-F.: Hybrid bat algorithm with artificial bee colony. In: Pan, J.-S., Snasel, V., Corchado, E.S., Abraham, A., Wang, S.-L. (Eds.), Intelligent Data analysis and its Applications, Volume II: Proceeding of the First Euro-China Conference on Intelligent Data Analysis and Applications, June 13–15, 2014, Shenzhen, China, pp. 45–55. Springer International Publishing, Cham (2014)Google Scholar
  43. 43.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    Tsai, C.-F., Dao, T.-K., Yang, W.-J., Nguyen, T.-T., Pan, T.-S.: Parallelized Bat algorithm with a communication strategy. In: Ali, M., Pan, J.-S., Chen, S.-M., Horng, M.-F. (Eds.), Modern Advances in Applied Intelligence: 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3–6, 2014, Proceedings, Part I, pp. 87–95. Springer International Publishing, Cham (2014)Google Scholar
  45. 45.
    Yılmaz, S., Küçüksille, E.U.: A new modification approach on bat algorithm for solving optimization problems. Appl. Soft Comput. 28, 259–275 (2015)CrossRefGoogle Scholar
  46. 46.
    Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inf. 1(4), 355–366 (2006)CrossRefGoogle Scholar
  47. 47.
    Chen, Y.T., Liao, B.Y., Lee, C.F., Tsay, W.D., Lai, M.C.: An adjustable frequency bat algorithm based on flight direction to improve solution accuracy for optimization problems. In: 2013 Second International Conference on Robot, Vision and Signal Processing, 10–12 Dec. 2013, pp. 172–177 (2013)Google Scholar
  48. 48.
    Wang, X., Wang, W., Wang, Y.: An adaptive bat algorithm. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (Eds.), Intelligent Computing Theories and Technology: 9th International Conference, ICIC 2013, Nanning, China, July 28–31, 2013. Proceedings, pp. 216–223. Springer, Heidelberg (2013)Google Scholar
  49. 49.
    Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evolut. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar
  50. 50.
    Fister, Jr I., Fong, S., Brest, J., Fister, I.: Towards the self-adaptation of the bat algorithm. In: Proceedings of the 13th IASTED International Conference on Artificial Intelligence and Applications (AIA 2014) Innsbruck the 13th IASTED International Conference on Artificial Intelligence and Applications (AIA 2014). IASTED, Feb 2014, pp. 400–406 (2014)Google Scholar
  51. 51.
    Yılmaz, S., Kucuksille, E.U., Cengiz, Y.: Modified bat algorithm. Elektronika ir. Elektrotechnika 20(2), 71–78 (2014)Google Scholar
  52. 52.
    Kabir, M.W.U., Alam, M.S.: Bat algorithm with self-adaptive mutation: a comparative study on numerical optimization problems. Int. J. Comput. Appl. 100(10), 7–13 (2014)Google Scholar
  53. 53.
    Xue, F., Cai, Y., Cao, Y., Cui, Z., Li, F.: Optimal parameter settings for bat algorithm. Int. J. Bio-Inspired Comput. 7(2), 125–128 (2015)CrossRefGoogle Scholar
  54. 54.
    Ross, P.J.: Taguchi techniques for quality engineering loss function, orthogonal experiments. Parameter and Tolerance Design (1996)Google Scholar
  55. 55.
    Pérez, J., Valdez, F.: Castillo O Modification of the Bat Algorithm using fuzzy logic for dynamical parameter adaptation. In: 2015 IEEE Congress on Evolutionary Computation (CEC), 25–28 May 2015, pp. 464–47 (2015)Google Scholar
  56. 56.
    Pérez, J., Valdez, F., Castillo, O.: Modification of the bat algorithm using Type-2 fuzzy logic for dynamical parameter adaptation. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems, pp. 343–355. Springer International Publishing, Cham (2017)CrossRefGoogle Scholar
  57. 57.
    Sabba, S., Chikhi, S.: A discrete binary version of bat algorithm for multidimensional knapsack problem. Int. J. Bio-Inspired Comput. 6(2), 140–152 (2014)CrossRefGoogle Scholar
  58. 58.
    Nakamura, R.Y.M., Pereira, L.A.M., Costa, K.A., Rodrigues, D., Papa, J.P., Yang, X.S.: BBA: A binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, 22–25 Aug. 2012, pp. 291–297 (2012)Google Scholar
  59. 59.
    Mirjalili, S., Mirjalili, S.M., Yang, X.-S.: Binary bat algorithm. Neural Comput. Appl. 25(3), 663–681 (2014)CrossRefGoogle Scholar
  60. 60.
    Huang, X., Zeng, X., Han, R.: Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search. Comput. Intell. Neurosci. 2017, 15 (2017)Google Scholar
  61. 61.
    Fister, I., Brest, J., Yang, X.S.: Modified bat algorithm with quaternion representation. In: 2015 IEEE Congress on Evolutionary Computation (CEC), 25–28 May 2015, pp. 491–498 (2015)Google Scholar
  62. 62.
    Afrabandpey, H., Ghaffari, M., Mirzaei, A., Safayani, M.: A novel Bat Algorithm based on chaos for optimization tasks. In: 2014 Iranian Conference on Intelligent Systems (ICIS), 4–6 Feb. 2014, pp. 1–6 (2014)Google Scholar
  63. 63.
    Abdel-Raouf, O., Abdel-Baset, M., El-Henawy, I.: An improved chaotic bat algorithm for solving integer programming problems. Int. J. Modern Educ. Comput. Sci. 6(8), 18 (2014)CrossRefGoogle Scholar
  64. 64.
    Lin, J.-H., Chou, C.-W., Yang, C.-H., Tsai, H.-L.: A chaotic Levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. Comput. Inf. Technol. 2(2), 56–63 (2012)Google Scholar
  65. 65.
    Rezaee Jordehi, A.: Chaotic bat swarm optimisation (CBSO). Appl. Soft Comput. 26, 523–530 (2015)CrossRefGoogle Scholar
  66. 66.
    Tsai, P.W., Zhang, J., Zhang, S., Liao, L.C., Pan, J.S., Istanda, V.: Deceleration convergence strategy for evolved bat algorithm. In: 2015 Third International Conference on Robot, Vision and Signal Processing (RVSP), 18–20 Nov. 2015, pp. 167–170 (2015)Google Scholar
  67. 67.
    Tsai, P.-W., Cai, S., Istanda, V., Liao, L.-C., Pan, J.-S.: Improving the searching capacity of evolved bat algorithm by the periodic signal. In: Zin, T.T., Lin, J.C.-W., Pan, J.-S., Tin, P., Yokota, M. (Eds.), Genetic and Evolutionary Computing: Proceedings of the Ninth International Conference on Genetic and Evolutionary Computing, August 26–28, 2015, Yangon, Myanmar – vol. 1. Springer International Publishing, Cham, pp. 3–9 (2016)Google Scholar
  68. 68.
    Wang, W., Wang, Y., Wang, X.: Bat Algorithm with recollection. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (eds.), Proceedings of Intelligent Computing Theories and Technology: 9th International Conference, ICIC 2013, Nanning, China, July 28–31, 2013, pp. 207–215. Springer, Heidelberg (2013)Google Scholar
  69. 69.
    Chen, Y.-T., Shieh, C.-S., Horng, M.-F., Liao, B.-Y., Pan, J.-S., Tsai, M.-T.: A guidable bat algorithm based on doppler effect to improve solving efficiency for optimization problems. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.), Proceedings of Computational Collective Intelligence. Technologies and Applications: 6th International Conference, ICCCI 2014, Seoul, Korea, September 24–26, 2014. Springer International Publishing, Cham, pp. 373–383 (2014)Google Scholar
  70. 70.
    Meng, X.-B., Gao, X.Z., Liu, Y., Zhang, H.: A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization. Expert Syst. Appl. 42(17), 6350–6364 (2015)CrossRefGoogle Scholar
  71. 71.
    Gehrt, S.D., Chelsvig, J.E.: Bat activity in an urban landscape: patterns at the landscape and micohabitat scale. Ecol. Appl. 13(4), 939–950 (2003)CrossRefGoogle Scholar
  72. 72.
    Cai, X., X-z, G., Xue, Y.: Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int. J. Bio-Inspired Comput. 8(4), 205–214 (2016)CrossRefGoogle Scholar
  73. 73.
    Wahm, G., Jantan, A.: An enhanced Bat algorithm with mutation operator for numerical optimization problems. Neural Comput. Appl., 1–35 (2017)Google Scholar
  74. 74.
    Neri, F., Mininno, E., Iacca, G.: Compact particle swarm optimization. Inf. Sci. 239, 96–121 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  75. 75.
    Dao, T.-K., Pan, J.-S., Nguyen, T.-T., Chu, S.-C., Shieh, C.-S.: Compact bat algorithm. In: Pan, J.-S., Snasel, V., Corchado, E.S., Abraham, A., Wang, S.-L. (eds.), Intelligent Data analysis and its Applications, Volume II: Proceeding of the First Euro-China Conference on Intelligent Data Analysis and Applications, June 13–15, 2014, Shenzhen, China. Springer International Publishing, Cham, pp. 57–68 (2014)Google Scholar
  76. 76.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report 2005005 (2005)Google Scholar
  77. 77.
    Meng, X.-B.: Novel Bat Algorithm (NBA). MathWorks. https://www.mathworks.com/matlabcentral/fileexchange/51258-novel-bat-algorithm–nba-?s_tid=srchtitle. Accessed May, 5th, 2017
  78. 78.
    Tabachnick BG, Fidell LS, Osterlind SJ (2001) Using multivariate statisticsGoogle Scholar
  79. 79.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut. Comput. 1(1), 3–18 (2011)CrossRefGoogle Scholar
  80. 80.
    Thanedar, P.B., Vanderplaats, G.N.: Survey of discrete variable optimization for structural design. J. Struct. Eng. 121(2), 301–306 (1995)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Industrial Mechanics Laboratory, Department of Mechanical EngineeringUniversity Badji Mokhtar of Annaba (UBMA)AnnabaAlgeria
  2. 2.Energy and Mechanical Engineering Laboratory, Department of Mechanical Engineering, Faculty of Engineering SciencesUniversity M’hamed Bougara of Boumerdes (UMBB) Avenue of IndependenceBoumerdesAlgeria
  3. 3.School of Science and TechnologyMiddlesex University LondonLondonUK

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