Skip to main content
SpringerLink
Log in

An effective fruit fly optimization algorithm with hybrid information exchange and its applications

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

As a newly proposed algorithm, fruit fly optimization algorithm (FOA) has been shown to have a strong capacity for solving numerical optimization problems. However, the basic FOA is faced with the challenges of poor diversity of the swarm and weak local search ability because of the improper osphresis operation and vision operation. To overcome these limitations synthetically, we propose an improved FOA based on hybrid location information exchange mechanism (HFOA) aiming at improving the swarm diversity in a more efficient way and well balance the global search and local search abilities. First, the proposed HFOA enables flies to communicate with each other and conduct local search in a swarm based approach. Second, osphresis operation is conducted in probability to balance the global search and local search processes. Finally, a mutation strategy called cataclysm policy is designed to help the flies jump out of the local extreme points. 18 complex continuous benchmark functions are used to test the performance of HFOA. Numerical experiments results indicate that HFOA outperforms main state-of-the-art algorithms. A classical non-deterministic polynomial hard problem—a widely-researched joint replenishment and delivery scheduling problem with resource restrictions is also used to further verify the ability of HFOA in solving practical operation management problems. Results show that HFOA can obtain lower operation cost than other widely used methods, demonstrating its ability to solve various complex optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(source: Wang et al. [43])

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Beezão AC, Cordeau JF, Laporte G, Yanasse HH (2017) Scheduling identical parallel machines with tooling constraints. Eur J Oper Res 257(3):834–844

    Article  MathSciNet  MATH  Google Scholar 

  2. Bandaru S, Ng AHC, Deb K (2017) Data mining methods for knowledge discovery in multi-objective optimization: Part A – Survey. Expert Systems Appl 70:139–159

    Article  Google Scholar 

  3. Bhuvana J, Aravindan C (2016) Memetic algorithm with preferential local search using adaptive weights for multi-objective optimization problems. Soft Comput 20(4):1365–1388

    Article  Google Scholar 

  4. Cha BC, Moon IK, Park JH (2008) The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system. Transp Res Part E: Logist Transp Rev 44(5):720–730

    Article  Google Scholar 

  5. 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(2L):459–465

    Article  Google Scholar 

  6. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  7. Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173

    Article  MathSciNet  MATH  Google Scholar 

  8. Chan CK, Li LY, Ng CT, Cheung BK, Langevin A (2006) Scheduling of multi-buyer joint replenishments. Int J Product Econ 102(1):132–142

    Article  Google Scholar 

  9. Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinf 12(4):458–473

    Article  Google Scholar 

  10. Gajjar S, Sarkar M, Dasgupta K (2016) FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Appl Soft Comput 43:235–247

    Article  Google Scholar 

  11. Ghareb AS, Bakar AA, Hamdan AR (2016) Hybrid feature selection based on enhanced genetic algorithm for text categorization’. Expert Systems Appl 49:31–47

    Article  Google Scholar 

  12. Goyal SK, Satir AT (1989) Joint replenishment inventory control: deterministic and stochastic models. Eur J Oper Res 38(1):2–13

    Article  MathSciNet  MATH  Google Scholar 

  13. Glover F (1989) Tabu search—Part I. Informs J Comput 1(1):89–98

    Google Scholar 

  14. Garg H (2016) A hybrid PSO-GA algorithm for constrained optimization problems. Appl Math Comput 274(11):292–305

    MathSciNet  Google Scholar 

  15. He Z, Qi H, Yao Y, Ruan L (2014) Inverse estimation of the particle size distribution using the fruit fly optimization algorithm. Appl Therm Eng 88:306–314

    Article  Google Scholar 

  16. Hong SP, Kim YH (2009) A genetic algorithm for joint replenishment based on the exact inventory cost. Computers Operations Research 36(1):167–175

    Article  MathSciNet  MATH  Google Scholar 

  17. Holland J (1994) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. Q Rev Biol 69(1):126–137

    Google Scholar 

  18. Kennedy J, Eberhart R (2011) Particle swarm optimization. Springer US

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

    Article  MathSciNet  MATH  Google Scholar 

  20. Khouja M, Goyal S (2008) A review of the joint replenishment problem literature: 1989–2005. Eur J Oper Res 186(1):1–16

    Article  MathSciNet  MATH  Google Scholar 

  21. Khouja M, Michalewicz Z, Satoskar SS (2000) A comparison between genetic algorithms and the RAND method for solving the joint replenishment problem. Prod Plan Control 11(6):556–564

    Article  Google Scholar 

  22. Li H, Guo S, Zhao H, Su C, Wang, B (2012) Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Energies 5(11): 4430–4445

  23. 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 Applic 22(3–4):783–791

    Article  Google Scholar 

  24. Moon IK, Cha BC (2006) The joint replenishment problem with resource restriction. Eur J Oper Res 173(1):190–198

    Article  MathSciNet  MATH  Google Scholar 

  25. Mitić M, Vuković N, Petrović M, Miljković Z (2015) Chaotic fruit fly optimization algorithm. Knowl-Based Syst 89:446–458

    Article  Google Scholar 

  26. Niu J, Zhong W, Liang Y, Luo N, Qian F (2015) Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization. Knowl-Based Syst 88:253–263

    Article  Google Scholar 

  27. 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 

  28. Pan QK, Sang HY, Duan JH, Gao L (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl-Based Syst 62:69–83

    Article  Google Scholar 

  29. Pan WT (2013) Using modified fruit fly optimisation algorithm to perform the function test and case studies. Connection. Science 25(2–3):151–160

    Google Scholar 

  30. Qu H, Wang L, Liu R (2015) A contrastive study of the stochastic location-inventory problem with joint replenishment and independent replenishment. Expert Systems Appl 42(4):2061–2072

    Article  Google Scholar 

  31. Rahman MM, Islam MM, Murase K, Yao X (2015) Layered ensemble architecture for time series forecasting. IEEE Transactions on Cybernetics 46(1):270–283

    Article  Google Scholar 

  32. Shan D, Cao GH, Dong HJ (2013) LGMS-FOA: an improved fruit fly optimization algorithm for solving optimization problems. Mathematical Problems in Engineering 2013(7): 1256–1271

  33. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  34. Sheng W, Bao Y (2013) Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle. Nonlinear. Dynamics 73(1–2):611–619

    MathSciNet  Google Scholar 

  35. Shu FT (1971) Economic ordering frequency for two items jointly replenished. Manage Sci 17(6):B406–B410

    Article  Google Scholar 

  36. Sindhuchao S, Romeijn HE, Akçali E, Boondiskulchok R (2005) An integrated inventory-routing system for multi-item joint replenishment with limited vehicle capacity. J Global Optim 32(1):93–118

    Article  MathSciNet  MATH  Google Scholar 

  37. Taormina R, Chau KW (2015) Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and extreme learning machines. J Hydrol 529(3):1617–1632

    Article  Google Scholar 

  38. Wang WC, Chau KW, Xu DM, Chen XY (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources. Management 29(8):2655–2675

    Google Scholar 

  39. Wu L, Zuo C, Zhang H (2015) A cloud model based fruit fly optimization algorithm. Knowl-Based Syst 89:603–617

    Article  Google Scholar 

  40. Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345

    Article  Google Scholar 

  41. Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93

    Article  Google Scholar 

  42. Wang L, Zheng XL, Wang SY (2013) A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowl-Based Syst 48:17–23

    Article  Google Scholar 

  43. Wang L, Shi Y, Liu S (2015) An improved fruit fly optimization algorithm and its application to joint replenishment problems. Expert Systems Appl 42(9):4310–4323

    Article  Google Scholar 

  44. Wang L, Liu R, Liu S (2016) An effective and efficient fruit fly optimization algorithm with level probability policy and its applications. Knowl-Based Syst 97:158–674

    Article  Google Scholar 

  45. Wang L, Dun CX, Lee CG, Fu QL, Zeng YR (2013) Model and algorithm for fuzzy joint replenishment and delivery scheduling without explicit membership function. Int J Adv Manuf Technol 66(9–12):1907–1920

    Article  Google Scholar 

  46. Wang L, Dun CX, Bi WJ, Zeng YR (2012) An effective and efficient differential evolution algorithm for the integrated stochastic joint replenishment and delivery model. Knowl-Based Syst 36:104–114

    Article  Google Scholar 

  47. Wang L, Liu R, Liu S (2017) Variable neighborhood search incorporating a new bounding procedure for joint replenishment and delivery problem. J Oper Res Soc. doi:10.1057/s41274-017-0188-5

    Google Scholar 

  48. Yan P, Wang G, Che A, Li YYR (2016) Hybrid discrete differential evolution algorithm for biobjective cyclic hoist scheduling with reentrance. Comput Oper Res 76:155–166

    Article  MathSciNet  MATH  Google Scholar 

  49. Yuan X, Dai X, Zhao J, He Q (2014) On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput 233(3):260–271

    MathSciNet  MATH  Google Scholar 

  50. Zeng YR, Peng L, Zhang J, Wang L (2016) An effective hybrid differential evolution algorithm incorporating simulated annealing for joint replenishment and delivery problem with trade credit. Int J Comput Intell Syst 9(6):1001–1015

    Article  Google Scholar 

  51. Zeng YR, Zeng Y, Choi BJ, Wang L (2017) Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy 127:381–396

    Article  Google Scholar 

  52. Zhang SW, Chau KW (2009) Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. Lect Notes Comput Sci 5754:948–955

    Article  Google Scholar 

  53. Zhang J, Chau KW (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J Universal Comput Sci 15(4):840–858

    Google Scholar 

  54. Zheng XL, Wang L, Wang SY (2014) A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl-Based Syst 57:95–103

    Article  Google Scholar 

  55. Zhang Y, Cui G, Wu J, Pan WT, He Q (2016) A novel multi-scale cooperative mutation fruit fly optimization algorithm. Knowl-Based Syst 114:24–35

    Article  Google Scholar 

Download references

Acknowledgements

This research is partially supported by National Natural Science Foundation of China (Nos: 71371080; 71531009), and Humanities and Social Sciences Foundation of Chinese Ministry of Education (No. 15YJA630095).

Author information

Authors and Affiliations

  1. School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China

    Sheng-Xiang Lv, Yu-Rong Zeng & Lin Wang

  2. School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China

    Yu-Rong Zeng

Authors
  1. Sheng-Xiang Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-Rong Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Wang.

Appendix

Appendix

See Fig. 10 and Tables 13, 14, 15.

Fig. 10
figure 10figure 10

Optimization process for F1-F18

Full size image
Table 13 Comparison results of benchmark functions (D = 30)
Full size table
Table 14 Comparison results of benchmark functions (D = 50)
Full size table
Table 15 Comparison results of benchmark functions (D = 100)
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, SX., Zeng, YR. & Wang, L. An effective fruit fly optimization algorithm with hybrid information exchange and its applications. Int. J. Mach. Learn. & Cyber. 9, 1623–1648 (2018). https://doi.org/10.1007/s13042-017-0669-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-017-0669-5

Keywords

Search

Navigation