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Bat algorithm based on simulated annealing and Gaussian perturbations

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Abstract

Bat algorithm (BA) is a new stochastic optimization technique for global optimization. In the paper, we introduce both simulated annealing and Gaussian perturbations into the standard bat algorithm so as to enhance its search performance. As a result, we propose a simulated annealing Gaussian bat algorithm (SAGBA) for global optimization. Our proposed algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also speeds up the global convergence rate. We have used BA, simulated annealing particle swarm optimization and SAGBA to carry out numerical experiments for 20 test benchmarks. Our simulation results show that the proposed SAGBA can indeed improve the global convergence. In addition, SAGBA is superior to the other two algorithms in terms of convergence and accuracy.

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References

  1. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO), vol 284. Springer, SCI, pp 65–74

  2. Yang XS (2011) Bat algorithm for multi-objective optimization. Int J Bio Inspired Comput 3(5):267–274

    Google Scholar 

  3. Li ZY, Ma L, Zhang HZ (2012) Genetic mutation bat algorithm for 0–1 knapsack problem. Comput Eng Appl 2012(35):1–10 (in Chinese)

  4. Lemma TA (2011) Use of fuzzy systems and bat algorithm for energy modeling in a gas turbine generator. In: IEEE Colloquium on Humanities, Science and Engineering, pp 305–310

  5. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  6. Mishra S, Shaw K, Mishra D (2012) A new metaheuristic classification approach for microarray data. Procedia Technol 4:802–806

    Article  Google Scholar 

  7. Khan K, Nikov A, Sahai A (2011) A fuzzy bat clustering method for ergonomic screening of office workplaces, S3T 2011. Adv Intell Soft Comput 101:59–66

    Article  Google Scholar 

  8. Khan K, Sahai A (2012) A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int J Intell Syst Appl (IJISA) 4(7):23–29

    Google Scholar 

  9. Altringham JD (1996) Bats: biology and behaviour. Oxford University Press, Oxford

    Google Scholar 

  10. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE, International Conference on Neural Networks, Perth, Australia

  11. Bertsimas D, Tsitsiklis J (1993) Simulated annealing. Int Stat Sci 8(1):10–15

    Article  Google Scholar 

  12. Zhiyuan W, Huihe S, Xinyu W (1997) Genetic annealing evolutionary algorithm. J ShangHai JiaoTong University (in China) 31(12):69–71

    Google Scholar 

  13. Xuemei Wang, Yihe Wang (1997) The combination of simulated annealing and genetic algorithms. Chin J Comput (in China) 20(4):381–384

    Google Scholar 

  14. Yang XS (2011) Review of meta-heuristic and generalised evolutionary walk algorithm. Int J Bio-Inspired Comput 3(2):77–84

    Article  Google Scholar 

  15. Gandomi AH, Yun GJ, Yang XS, Talatahari S (2013) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simul 18(2):327–340

    Article  MATH  MathSciNet  Google Scholar 

  16. Yang XS, Deb S (2012) Two-stage eagle strategy with differential evolution. Int J Bio-Inspired Comput 4(1):1–5

    Article  MathSciNet  Google Scholar 

  17. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    Article  MathSciNet  Google Scholar 

  18. Gandomi AH, Yang XS, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1):191–200

    Article  MATH  MathSciNet  Google Scholar 

  19. Zhao S, Huang G (2006) Design and study of particle swarm optimization with simulated annealing. J Baise University 19(6):9–12

    MathSciNet  Google Scholar 

  20. Gong C, Wang Z (2009) Proficient in MATLAB. Beijing: Publishing House of Electronics Industry (in China), pp 309–312

  21. Hedar J Test functions for unconstrained global optimization [DB/OL]. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO_files/Page364.htm

  22. Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):150–194

    MATH  Google Scholar 

  23. Fisher RA (1925) Theory of statistical estimation. Proceed Camb Philos Soc 22:700–715

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the financial support by Shaanxi Provincial Soft Science Foundation (2012KRM58) and Shaanxi Provincial Education Grant (12JK0744 and 11JK0188).

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Authors and Affiliations

  1. School of Science, Xi’an Polytechnic University, Xi’an, People’s Republic of China

    Xing-shi He, Wen-Jing Ding & Xin-She Yang

  2. School of Science and Technology, Middlesex University, London, NW4 4BT, UK

    Xin-She Yang

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  1. Xing-shi He
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  2. Wen-Jing Ding
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Correspondence to Xing-shi He.

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He, Xs., Ding, WJ. & Yang, XS. Bat algorithm based on simulated annealing and Gaussian perturbations. Neural Comput & Applic 25, 459–468 (2014). https://doi.org/10.1007/s00521-013-1518-4

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  • DOI: https://doi.org/10.1007/s00521-013-1518-4

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