Soft Computing

, Volume 21, Issue 7, pp 1877–1893 | Cite as

Bimodal fruit fly optimization algorithm based on cloud model learning

  • Lianghong Wu
  • Cili Zuo
  • Hongqiang Zhang
  • Zhaohua Liu
Methodologies and Application

Abstract

The Fruit Fly Optimization Algorithm (FOA) is one of the latest swarm intelligence-based methods inspired by the foraging behavior of fruit fly swarm. To improve the global search ability and solution accuracy of the FOA, a bimodal adaptive fruit fly optimization algorithm using normal cloud learning (BCMFOA) is proposed in this paper. Based on the labor allocation characteristics of the swarm foraging behavior, the fruit fly population is divided into two groups in the optimization process according to their duties of searching or capturing. The search group is mainly based on the fruit fly’s olfactory sensors to find possible global optima in a large range, while the capture group makes use of their keen visions to exploit neighborhood of the current best food source found by the search group. Moreover, the randomness and fuzziness of the foraging behavior of fruit fly swarm during the olfactory phase are described by a normal cloud model. Using a normal cloud generator and an adaptive parameter updation strategy, the search range of the fruit fly population is adaptively adjusted. Therefore, the ability of FOA to avoid local optima is enhanced greatly. Twenty-three benchmark functions are used to test the performance of the proposed BCMFOA algorithm. Numerical results show that the proposed method can significantly improve the global search ability and solution accuracy of FOA. Compared with existing methods such as PSO, DE, AFAS, the experimental results indicate that BCMFOA has better or comparative convergence performance and accuracy. The application to the multi-parameter estimation of a permanent magnet synchronous motor further confirms its good performance.

Keywords

Fruit fly optimization algorithm Bimodal Normal cloud model Parameter adaptive 

References

  1. Alcalá-Fdez J, Sánchez L, García S et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318CrossRefGoogle Scholar
  2. Chen PW, Lin WY, Huang TH, Pan WT (2013) Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service. Appl Math Inf Sci 7(2):459–465CrossRefGoogle Scholar
  3. Dai H, Zhao G, Lu J et al (2014) Comment and improvement on “A new fruit fly optimization algorithm: taking the financial distress model as an example”. Knowl-Based Syst 59:159–160CrossRefGoogle Scholar
  4. Derrac J, García S, Molina D et al (2011) 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):3–18CrossRefGoogle Scholar
  5. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41CrossRefGoogle Scholar
  6. El-Sousy FFM (2011) Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive. IET Elect Power Appl 5(1):113–132CrossRefGoogle Scholar
  7. Han J, Wang P, Yang X (2012) Tuning of PID controller based on fruit fly optimization algorithm. In: International conference on mechatronics and automation (ICMA), pp 409–413Google Scholar
  8. Jiang M, Yuan D, Cheng Y (2009) Improved artificial fish swarm algorithm. In: IEEE fifth international conference on natural computation (ICNC’09), vol 4, pp 281–285Google Scholar
  9. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetMATHGoogle Scholar
  10. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings IEEE international conference neural network, vol 4, pp 1942–1948Google Scholar
  11. Li D, Meng H, Shi X (1995) Membership clouds and membership clouds generator. J Comput Res Dev 32(6):15–20Google Scholar
  12. Li D, Liu C, Gan W (2009) A new cognitive model: cloud model. Int J Intell Syst 24(3):357–375CrossRefMATHGoogle Scholar
  13. Li C, Xu S, Li W, Hu L (2012) A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller. J Converg Inf Technol 7:69–77Google Scholar
  14. Li H, Guo S, Li C, Sun J (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst 37:378–387CrossRefGoogle Scholar
  15. Liang JJ, Qin AK, Suganthan PN et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295CrossRefGoogle Scholar
  16. Lima AMN, Jacobina CB, Filho EBDS (1997) Nonlinear parameter estimation of steady-state induction machine models. IEEE Trans Ind Electron 44(3):390–397CrossRefGoogle Scholar
  17. Lin SM (2013) Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network. Neural Comput Appl 7:459–465Google Scholar
  18. Liu K, Zhang Q, Chen JT et al (2011) Online multiparameter estimation of nonsalient-pole PM synchronous machines with temperature variation tracking. IEEE Trans Ind Electron 58(5):1776–1788CrossRefGoogle Scholar
  19. Liu ZH, Zhang J, Zhou SW et al (2013) Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM. IEEE Trans Cybern 43(6):1921–1934CrossRefGoogle Scholar
  20. Liu K, Zhu ZQ, Zhang J (2010) Multi-parameter estimation of nonsalient pole permanent magnet synchronous machines by using evolutionary algorithms. In: Proc. Changsha, China, IEEE Int. Conf. BIC-TA, pp 766–774Google Scholar
  21. Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42:965–997CrossRefGoogle Scholar
  22. Pan WT (2011) A new evolutionary computation approach: fruit fly optimization algorithm. In: 2011 conference of digital technology and innovation management, TaipeiGoogle Scholar
  23. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model. Knowl-Based Syst 26:69–74CrossRefGoogle Scholar
  24. Pan WT (2013) Using modified fruit fly optimization algorithm to perform the function test and case studies. Connect Sci 25(2–3):151–160CrossRefGoogle Scholar
  25. Pan QK, Sang HY, Duan JH et al (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl-Based Syst 62:69–83CrossRefGoogle Scholar
  26. Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67MathSciNetCrossRefGoogle Scholar
  27. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417CrossRefGoogle Scholar
  28. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518CrossRefGoogle Scholar
  29. Shan D, Cao GH, Dong H (2013) LGMS-FOA an improved fruit fly optimization algorithm for solving optimization problems. Math Prob Eng 2013. doi:10.1155/2013/108768
  30. Sheng W, Bao Y (2013) Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle. Nonlinear Dyn 73(1–2):611–619MathSciNetCrossRefGoogle Scholar
  31. Underwood SJ, Husain I (2010) Online parameter estimation and adaptive control of permanent-magnet synchronous machines. IEEE Trans Ind Electron 57(7):2435–2443CrossRefGoogle Scholar
  32. Viswanathan GM (1999) Optimizing the success of random searches. Nature 401:911–914CrossRefGoogle Scholar
  33. Wang L, Zheng XL, Wang SY (2013) A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowl-Based Syst 48:17–23CrossRefGoogle Scholar
  34. Yuan X, Dai X, Zhao J et al (2014) On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput 233:260–271Google Scholar
  35. Zheng XL, Wang L, Wang SY (2014) A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl-Based Syst 57:95–103CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Lianghong Wu
    • 1
  • Cili Zuo
    • 1
  • Hongqiang Zhang
    • 1
  • Zhaohua Liu
    • 1
  1. 1.School of Information and Electrical EngineeringHunan University of Science and TechnologyXiangtanPeople’s Republic of China

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