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Neural Computing and Applications

, Volume 31, Issue 3, pp 873–885 | Cite as

A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

  • Seyed Mohsen Mousavi
  • Madjid TavanaEmail author
  • Najmeh Alikar
  • Mostafa Zandieh
Original Article

Abstract

Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.

Keywords

Fuzzy rule-based system Classification system Fruit fly optimization Hybrid algorithm Taguchi method 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Alikar N, Abdullah S, Mousavi SM, Niaki STA (2013) A hybrid particle swarm optimization and fuzzy rule-based system for breast cancer diagnosis. Int J Soft Comput 8(2):126–133Google Scholar
  2. 2.
    Cintra M, Camargo H, Monard M (2016) Genetic generation of fuzzy systems with rule extraction using formal concept analysis. Inf Sci 349:199–215CrossRefGoogle Scholar
  3. 3.
    Cochran WG, Cox GM (1957) Experimental designs, 2nd edn. Wiley, New YorkGoogle Scholar
  4. 4.
    Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1–30MathSciNetzbMATHGoogle Scholar
  5. 5.
    Derhami S, Smith AE (2017) An integer programming approach for fuzzy rule-based classification systems. Eur J Oper Res 256:924–934Google Scholar
  6. 6.
    Derhami S, Smith AE (2016) A technical note on the paper “hGA: hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems”. Appl Soft Comput 41:91–93CrossRefGoogle Scholar
  7. 7.
    Elkano M, Galar M, Sanz J, Bustince H (2016) Fuzzy rule-based classification systems for multi-class problems using binary decomposition strategies: on the influence of n-dimensional overlap functions in the fuzzy reasoning method. Inf Sci 332:94–114CrossRefGoogle Scholar
  8. 8.
    Fallahpour A, Moghassem A (2013) Yarn strength modelling using adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). J Eng Fabr Fibers (JEFF) 8(4):6–18Google Scholar
  9. 9.
    Fazzolari M, Alcalá R, Herrera F (2014) A multi-objective evolutionary method for learning granularities based on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: D-MOFARC algorithm. Appl Soft Comput 24:470–481CrossRefGoogle Scholar
  10. 10.
    Fazzolari M, Giglio B, Alcalá R, Marcelloni F, Herrera F (2013) A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: accuracy-complexity trade-off. Knowl Based Syst 54:32–41CrossRefGoogle Scholar
  11. 11.
    Gorzałczany MB, Rudziński F (2016) A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Appl Soft Comput 40:206–220CrossRefGoogle Scholar
  12. 12.
    Ishibuchi H, Nakaskima T (1999) Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans Ind Electron 46(6):1057–1068CrossRefGoogle Scholar
  13. 13.
    Khanlou HM, Ang BC, Talebian S, Afifi AM, Andriyana A (2015) Electrospinning of polymethyl methacrylate nanofibers: optimization of processing parameters using the Taguchi design of experiments. Text Res J 85:356–368CrossRefGoogle Scholar
  14. 14.
    Khanlou MH, Ang BC, Talebian S, Barzani MM, Silakhori M, Fauzi H (2015) Multi-response analysis in the processing of poly (methyl methacrylate) nano-fibres membrane by electrospinning based on response surface methodology: fibre diameter and bead formation. Measurement 65:193–206CrossRefGoogle Scholar
  15. 15.
    Keshtegar B, Heddam S (2017) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Appl. doi: 10.1007/s00521-017-2917-8 Google Scholar
  16. 16.
    Khanlou MH, Ang BC, Kim JH, Talebian S, Ghadimi A (2014) Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. Neural Comput Appl 25:767–777CrossRefGoogle Scholar
  17. 17.
    Khanlou MH, Sadollah A, Ang BC, Barzani MM, Silakhori M, Talebian S (2015) Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System. Neural Comput Appl 26:1751–1761CrossRefGoogle Scholar
  18. 18.
    Lei X, Ding Y, Fujita H, Zhang A (2016) Identification of dynamic protein complexes based on fruit fly optimization algorithm. Knowl Based Syst 105:270–277CrossRefGoogle Scholar
  19. 19.
    Montgomery DC (2008) Design and analysis of experiments. Wiley, New YorkGoogle Scholar
  20. 20.
    Mousavi SM, Alikar N, Niaki STA (2016) An improved fruit fly optimization algorithm to solve the homogeneous fuzzy series–parallel redundancy allocation problem under discount strategies. Soft Comput 20(6):2281–2307CrossRefGoogle Scholar
  21. 21.
    Mousavi SM, Sadeghi J, Niaki STA, Tavana M (2016) A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO. Appl Soft Comput 43:57–72CrossRefGoogle Scholar
  22. 22.
    Mousavi SM, Alikar N, Niaki STA, Bahreininejad A (2015) Optimizing a location allocation-inventory problem in a two-echelon supply chain network: a modified fruit fly optimization algorithm. Comput Ind Eng 87:543–560CrossRefGoogle Scholar
  23. 23.
    Mousavi SM, Hajipour V, Niaki STA, Alikar N (2013) Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: two calibrated meta-heuristic algorithms. Appl Math Model 37:2241–2256Google Scholar
  24. 24.
    Mousavi SM, Hajipour V, Niaki STA, Aalikar N (2014). A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm. Int J Adv Manuf Technol 70(9–12):1739–1756Google Scholar
  25. 25.
    Mousavi SM, Hajipour V, Niaki STA, Alikar N (2013) Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: two calibrated meta-heuristic algorithms. Appl Math Model 37(4):2241–2256MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Naka S, Genji T, Yura T, Fukuyama Y (2001) Practical distribution state estimation using hybrid particle swarm optimization. In: Proceeding IEEE Power engineering society winter meeting, vol 2. USA, p 815–820Google Scholar
  27. 27.
    Ong P, Chi DDVS, Ho CS, Ng CH (2016) Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches. Neural Comput Appl. doi: 10.1007/s00521-016-2626-8 Google Scholar
  28. 28.
    Pasandideh SHR, Niaki STA, Mousavi SM (2013) Two metaheuristics to solve a multi-item multiperiod inventory control problem under storage constraint and discounts. Int J Adv Manuf Technol 69(5–8):1671–1684Google Scholar
  29. 29.
    Pan Q-K, Sang H-Y, Duan J-H, Gao L (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl-Based Syst 62:69–83CrossRefGoogle Scholar
  30. 30.
    Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74CrossRefGoogle Scholar
  31. 31.
    Phadke MS (1995) Quality engineering using robust design. Prentice Hall PTR, Englewood CliffsGoogle Scholar
  32. 32.
    Pourpanah F, Lim CP, Saleh JM (2016) A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction. Expert Syst Appl 49:74–85CrossRefGoogle Scholar
  33. 33.
    Ross PJ (1996) Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design. McGraw-Hill Professional, New YorkGoogle Scholar
  34. 34.
    Rudziński F (2016) A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl Soft Comput 38:118–133CrossRefGoogle Scholar
  35. 35.
    Sanz J, Fernández A, Bustince H, Herrera F (2011) A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position. Int J Approx Reason 52(6):751–766CrossRefGoogle Scholar
  36. 36.
    Sanz JA, Fernandez A, Bustince H, Herrera F (2013) IVTURS: a linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection. IEEE Trans Fuzzy Syst 21(3):399–411CrossRefGoogle Scholar
  37. 37.
    Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH (2009) PID: the pathway interaction database. Nucleic Acids Res 37(suppl 1):D674–D679CrossRefGoogle Scholar
  38. 38.
    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
  39. 39.
    Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol 3. p 1945–1950Google Scholar
  40. 40.
    Singh S, Olugu EU, Fallahpour A (2014) Fuzzy-based sustainable manufacturing assessment model for SMEs. Clean Technol Environ Policy 16(5):847–860CrossRefGoogle Scholar
  41. 41.
    Štepnicka M, Burda M, Štepnicková L (2015) Fuzzy rule base ensemble generated from data by linguistic associations mining. Fuzzy Sets Syst 285:140–161Google Scholar
  42. 42.
    Sugeno M (1985) An introductory survey of fuzzy control. Inf Sci 36(1):59–83MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Tsakiridis NL, Theocharis JB, Zalidis GC (2016) DECO 3 R: a differential evolution-based algorithm for generating compact fuzzy rule-based classification systems. Knowl Based Syst 105:160–174CrossRefGoogle Scholar
  44. 44.
    Wang L, Shi Y, Liu S (2015) An improved fruit fly optimization algorithm and its application to joint replenishment problems. Expert Syst Appl 42(9):4310–4323CrossRefGoogle Scholar
  45. 45.
    Zheng X-L, Wang L (2016) A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints. Expert Syst Appl 65:28–39CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Seyed Mohsen Mousavi
    • 1
  • Madjid Tavana
    • 2
    • 3
    Email author
  • Najmeh Alikar
    • 1
  • Mostafa Zandieh
    • 4
  1. 1.Department of Mechanical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Business Systems and Analytics Department, Distinguished Chair of Business AnalyticsLa Salle UniversityPhiladelphiaUSA
  3. 3.Business Information Systems Department, Faculty of Business Administration and EconomicsUniversity of PaderbornPaderbornGermany
  4. 4.Department of Industrial Management, Management and Accounting FacultyShahid Beheshti UniversityTehranIran

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