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A novel 3D fruit fly optimization algorithm and its applications in economics

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Abstract

Fruit fly optimization algorithm (FOA) is a method that we have previously developed from the food-finding behavior of fruit flies to solve optimization problems. The advantage of FOA is simple and easy to understand compared to traditional stochastic algorithms. In this paper, we propose a modified algorithm called novel 3D-FOA. The performances of the 3D-FOA are far better than those of the original FOA. We select more than thirty different nonlinear functions as test vehicles to show that the search efficiency and/or quality of the 3D-FOA is superior to that of the genetic algorithm and particle swarm optimization algorithm. We also apply the 3D-FOA on some economics topics, two theoretic examples and a case study. Our results strongly suggest that the 3D-FOA can enhance capabilities in a variety of fields and future experiments.

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References

  1. Altman EI, Marco G, Varetto F (1994) Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks. J Bank Finance 18(3):505–529

    Article  Google Scholar 

  2. Aryanezhad MB, Hemati M (2008) A new genetic algorithm for solving nonconvex nonlinear programming problems. Appl Math Comput 199(1):186–194

    MathSciNet  MATH  Google Scholar 

  3. Bao L, Zeng JC (2009) Comparison and analysis of the selection mechanism in the artificial bee colony algorithm. In: Ninth international conference on hybrid intelligent systems, pp 411–416

  4. Chambers L (ed) (1995) Practical handbook of genetic algorithms: applications, vol 1. CRC Press, Boca Raton

    MATH  Google Scholar 

  5. Chen SH, Lin WY, Taso CI (1999) Genetic algorithms, trading strategies and stochastic processes: some new evidences from Mote Carlo simulations. In: GECCO99, Proceedings of the genetic and evolutionary computation conference, July 13–17, Morgan Kaufmann, Orlando

  6. Chen WN, Zhang J (2010) A novel set-based particle swarm optimization method for discrete optimization problem. IEEE Trans Evol Comput 14(2):278–300

    Article  Google Scholar 

  7. Chiang AC, Wainwright K (2005) Fundamental methods of mathematical economics, 4th edn. McGraw Hill, New York

    Google Scholar 

  8. Chipperfield A, Fleming P, Pohlheim H, Fonseca C (1994) Genetic algorithm toolbox for use with MATLAB, user’s guide ver.1.2. Department of automatic control and systems engineering, University of Sheffield

  9. Chtioui Y, Bertrand D, Barba D (1998) Feature selection by a genetic algorithm application to seed discrimination by artificial vision. J Sci Food Agric 76(1):77–86

    Article  Google Scholar 

  10. Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Proceedings of 9th Pacific Rim international conference on artificial intelligence, Guilin, China, LNAI 4099, pp 854–858

  11. Clerc M, Telecom F, France A, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73. doi:10.1109/4235.985692

    Article  Google Scholar 

  12. Coats PK, Fant LF (1993) Recognizing financial distress patterns using a neural network tool. Financ Manage 22(3):142–155

    Article  Google Scholar 

  13. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  14. Eberhart RC, Kenndy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, pp 39–43

  15. Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the congress on evolutionary computation, San Diego, pp 84–88

  16. Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225

    Article  Google Scholar 

  17. Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) (1996) Advances in knowledge discovery and data mining Cambridge. MIT, MA

    Google Scholar 

  18. Fayyad UM, Stolorz P (1997) Data mining and KDD: promise and challenges. Futur Gener Comput Syst 13(2–3):99–115

    Article  Google Scholar 

  19. Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE, LA

    MATH  Google Scholar 

  20. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67

    Article  MathSciNet  MATH  Google Scholar 

  21. DE Goldberg (1989) Genetic algorithms in search optimization & machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  22. Haiyan Q, Xinling S (2008) On the analysis of performance of the improved artificial-bee-colony algorithm. In: Fourth international conference on natural computation, pp 654–657

  23. He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Article  Google Scholar 

  24. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan, Ann Arbor

    Google Scholar 

  25. Huang CJ, Chen PW, and Pan WT (2011) Multi-stage data mining technique to build the forecast model for Taiwan stocks. Neural Comput Online First TM

  26. Huang TH, Lin WY (2013) Groups wisdom—3D fruit fly optimization algorithm for financial distress forecasting. JCIT 8(11):536–546

    Article  Google Scholar 

  27. Jiang M, Cheng Y (2010) Simulated annealing artificial fish swarm algorithm. In: IEEE 8th world congress on intelligent control and automation (WCICA), Jinan, pp 1590–1593

  28. Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13–14):861–870

    Article  Google Scholar 

  29. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  30. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  31. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of 1995 IEEE international conference on neural networks, IEEE, Piscataway, pp 1942–1948

  32. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  33. Lewis PAW, Stevens JG (1991) Nonlinear modeling of time series using multivariate adaptive regression splines (MARS). JASA 86(416):864–877

    Article  MATH  Google Scholar 

  34. Li XL (2003) A new intelligent optimization-artificial fish swarm algorithm. Ph.D thesis, Zhejiang University (in Chinese)

  35. Lin WY (2000) Application of artificial intelligence in investment strategies. Hwa Tai, Taiwan (in Chinese)

  36. Lin WY, Huang TH (2014) A hybrid approach of 3-D fruit fly optimization algorithm and general regression neural network for financial distress forecasting. JDCTA 8(4):1–14

    MathSciNet  Google Scholar 

  37. Liu B, Wang L, Jin YH, Tang F, Huang DX (2005) Improved particle swarm optimization combined with chaos. Chaos Solut Fractals 25(5):1261–1271

    Article  MATH  Google Scholar 

  38. Liu Y, Passino KM (2002) Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J Optim Theory Appl 115(3):603–628

    Article  MathSciNet  MATH  Google Scholar 

  39. Michalewicz A (1992) Genetic algorithms + data structures = evolution programs. Springer, New York

    Book  MATH  Google Scholar 

  40. Mills DS, Marchant-Forde JN, McGreevy PD, Morton DB, Nicol CJ, Phillips CJC et al (eds) (2010) The encyclopedia of applied animal behavior and welfare. CAB International, UK

    Google Scholar 

  41. Millor J, Ame JM, Halloy J, Deneubourg JL (2006) Individual discrimination capability and collective decision-making. J Theor Biol 239(3):313–323

    Article  MathSciNet  Google Scholar 

  42. Miranda V, Keko H, Duque ÁJ (2008) Stochastic star communication topology in evolutionary particle swarms (EPSO). Int J Comput Intell Res 4(2):105–116

    MathSciNet  Google Scholar 

  43. Munan L (2014) Three-dimensional path planning of Robots in virtual situations based on an improved fruit fly optimization algorithm. In: Hindawi Publishing Corporation Advances in Mechanical Engineering vol 2014, Article ID 314797, doi:10.1155/2014/314797

  44. Nien B (2011) Application of data mining and fruit fly optimization algorithm to construct financial crisis earlywarning model—a case study of listed companies in Taiwan. Master Thesis, Department of Economics, Soochow University, Taiwan (in Chinese)

  45. Odom MD, Sharda R (1990) A neural network model for bankruptcy prediction. In: IJCNN international joint conference, vol 2, pp 163–168

  46. Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18(1):109–131

    Article  MathSciNet  Google Scholar 

  47. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

    Article  Google Scholar 

  48. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67

    Article  MathSciNet  Google Scholar 

  49. Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evolut Appl. doi:10.1155/2008/685175

    Google Scholar 

  50. Ravisankar P, Ravi V (2010) Financial distress prediction in banks using group method of data handling neural network, Counter propagation neural network and fuzzy ARTMAP. Knowl Based Syst 23(8):823–831

    Article  Google Scholar 

  51. Reilly SM, Jorgensen ME (2011) The evolution of jumping in frogs: morphological evidence for the basal anuran locomotor condition and the radiation of locomotor systems in crown group anurans. J Morphol 272(2):149–168

    Article  Google Scholar 

  52. Shi F et al. (2010) MATLAB neural network analysis of 30 cases. Beijing University of Aeronautics and Astronautics (in Chinese)

  53. Shieh HL, Kuo CC, Chiang CM (2011) Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification. Appl Math Comput 218(8):4365–4383

    MATH  Google Scholar 

  54. Specht DF (1990) Probabilisticneuralnetworksandthepolynomialadalineascomplementarytechniquesforclassification. IEEE Trans Neural Netw l(1):111–121

    Article  Google Scholar 

  55. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  Google Scholar 

  56. Srinivasan D, Loo, WH, Cheu RL (2003) Traffic incident detection using particle swarm optimization. In: Proceedings of the 2003 IEEE swarm intelligence symposium, pp 144–151

  57. Tai CC (2010) Application of artificial neural networks and genetic algorithms to construct models of financial distress—a case study of listed companies in Taiwan. Master Thesis, Department of Economics, Soochow University, Taiwan (in Chinese)

  58. Tam KY, Kiang M (1992) Managerial applications of neural networks: the case of bank failure predictions. Manag Sci 38(7):926–947

    Article  MATH  Google Scholar 

  59. Theodossiou PT (1993) Predicting shifts in the mean of a multivariate time series process: an application in predicting business failures. JASA 88(442):441–449

    Article  Google Scholar 

  60. Tsai PW, Pan JS, Chen SM, Liao BY, Hao, SP (2008) Parallel cat swarm optimization. In: Proceedings of 7th international conference on machine learning and cybernetics, Kunming, pp 3328–3333

  61. Wang M, Zang XZ, Fan JZ, Zhao J (2008) Biological jumping mechanism analysis and modeling for frog robot. J Bionic Eng 5(3):181–188

    Article  Google Scholar 

  62. Wu CY (2004) Using non-financial information to predict bankruptcy: a study of public companies in Taiwan. Int J Manage 21(2):194–202

    Google Scholar 

  63. Yang ZR, Platt MB, Platt HD (1999) Probabilistic neural networks in bankruptcy prediction. J Bus Res 44(2):67–74

    Article  Google Scholar 

Download references

Acknowledgments

The author expresses sincere thanks to the anonymous referees and my students Tsui-Hua Huang and Benjamin Nien, with whose valuable help the quality of the paper has been very much improved.

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  1. Department of Economics, Soochow University, Taipei, Taiwan

    Wei-Yuan Lin

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  1. Wei-Yuan Lin
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Correspondence to Wei-Yuan Lin.

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Lin, WY. A novel 3D fruit fly optimization algorithm and its applications in economics. Neural Comput & Applic 27, 1391–1413 (2016). https://doi.org/10.1007/s00521-015-1942-8

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  • DOI: https://doi.org/10.1007/s00521-015-1942-8

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