Skip to main content
SpringerLink
Log in

Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

  • Conference paper
  • First Online:
Data Science (ICPCSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1451))

  • 1016 Accesses

Abstract

To solve the emerging complex optimization problems, multi objective optimization algorithms are needed. By introducing the surrogate model for approximate fitness calculation, the multi objective firefly algorithm with surrogate model (MOFA-SM) is proposed in this paper. Firstly, the population was initialized according to the chaotic mapping. Secondly, the external archive was constructed based on the preference sorting, with the lightweight clustering pruning strategy. In the process of evolution, the elite solutions selected from archive were used to guide the movement to search optimal solutions. Simulation results show that the proposed algorithm can achieve better performance in terms of convergence iteration and stability.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Harman, M., Jones, B.F.: Search-based software engineering. Inf. Softw. Technol. 43(14), 833–839 (2001)

    Google Scholar 

  2. Harman, M., Yue, J, Zhang, Y.: Achievements, open problems and challenges for search based software testing. In: 8th IEEE International Conference on Software Testing, Verification and Validation (ICST 2015), Graz, Austria (2015)

    Google Scholar 

  3. Fraser, G., Arcuri, A.: Whole test suite generation. IEEE Trans. Software Eng. 39(2), 276–291 (2013)

    Article  Google Scholar 

  4. Fraser, G., Arcuri, A., Mcminn, P.: A memetic algorithm for whole test suite generation. J. Syst. Softw. 103(2), 311–327 (2015)

    Article  Google Scholar 

  5. Panichella, A., Kifetew, F.M., Tonella, P.: Reformulating branch coverage as a many-objective optimization problem. In: IEEE International Conference on Software Testing. IEEE (2015)

    Google Scholar 

  6. Panichella, A., Kifetew, F., Tonella, P.: Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans. Softw. Eng. 44, 1 (2018)

    Google Scholar 

  7. Xie, C.W., Xiao, C., Ding, L.X., Xia, X.W., Zhu, J.Y., Zhang, F.L.: HMOFA: a hybrid multi-objective firefly algorithm. J. Softw. 29(4), 1143–1162 (2018)

    MathSciNet  Google Scholar 

  8. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)

    Article  Google Scholar 

  9. Srinivas, N., Deb, K.: Multi-Objective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  10. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Google Scholar 

  11. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: Giannakoglou, K., Tsahalis, D.T., Periaux, J., Papailious, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100. Springer, Berlin (2002). https://doi.org/10.3929/ethz-a-004284029

    Chapter  Google Scholar 

  12. Xie, C., Feilong, Z., Jianbo, L., et al.: Multi-objective firefly algorithm based on multiply cooperative strategies. Acta Electron. Sin. 47(11), 2359–2367 (2019)

    Google Scholar 

  13. Zhang, Q., Hui, L.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2008)

    Article  Google Scholar 

  14. Coello, C.C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particles swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)

    Article  Google Scholar 

  15. Chengwang, X., Lei, X., Huairui, Z., et al.: Multi-objective fireworks optimization algorithm using elite opposite based learning. Acta Electronica Sinica 44(5), 1180–1188 (2016)

    Google Scholar 

  16. Gopi, P., Ramalingam, M., Arumugam, C.: Search based test data generation: a multi objective approach using MOPSO evolutionary algorithm (2016)

    Google Scholar 

  17. Zhiqun, Z.: Test case generation based on multi-objective evolutionary algorithm. Hunan University (2016)

    Google Scholar 

  18. Dafei, H.: An improved evolutionary strategy for multi-target path coverage testing. Jiangxi University of Finance and Economics (2019)

    Google Scholar 

  19. Shunhui, J., Pengcheng, Z.: Test case generation approach for data flow based on dominance relations. Comput. Sci. 47(9), 40–46 (2020)

    Google Scholar 

  20. Miao, Z., Shujuan, J., Yanmei, Z.: Research on multi-objective optimization in class integration test order. J. Chin. Comput. Syst. 38(8), 1772–1777 (2017)

    Google Scholar 

  21. Weizhi, L., Xiaoyun, X., Xiaojun, J.: Test data generation for multiple paths coverage based on ant colony algorithm. Acta Electronica Sinica 48(7), 1330–1342 (2020)

    Google Scholar 

  22. Chengwang, X., Weiwei, Y., Yingzhou, B., et al.: Many-objective evolutionary algorithm based on decomposition and coevolution. J. Softw. 31(2), 356–373 (2020)

    Google Scholar 

  23. Yang, X.S.: Nature-inspired Metaheuristic Algorithms. Luniver Press, London (2008)

    Google Scholar 

  24. Dunwei, G., Yan, Z.: Novel evolutionary generation approach to test data for multiple paths coverage. Acta Electron. Sin. 038(006), 1299–1304 (2010)

    Google Scholar 

  25. Gaoyang, L.: The research of swarm intelligence optimization algorithms based on surrogate model. Jilin University (2016)

    Google Scholar 

  26. Xiangjuan, Y., Dunwei, G., Bin, L.: Evolutional test data generation for path coverage by integrating neural network. J. Softw. 27(4), 828–838 (2016)

    Google Scholar 

  27. Xiaoji, C., Chuan, S.: Multiobjective evolutionary algorithm based on hybrid individual selection mechanism. J. Softw. 30(12), 3651–3664 (2019)

    Google Scholar 

  28. Xunxue, C., Chuang, L.: A preference based multi-objective concordance genetic algorithm. J. Softw. 16(005), 761–770 (2005)

    Article  MathSciNet  Google Scholar 

  29. Dongdong, Y., Licheng, J., Maoguo, G., et al.: Clone selection algorithm to solve preference multi-objective optimization. J. Softw. 021(001), 14–33 (2010)

    Article  MathSciNet  Google Scholar 

  30. Laumanns, M., Thiele, L., Deb, K., et al.: Combining convergence and diversity in evolutionary multi-objective optimization. Evol. Comput. 10(3), 263–282 (2002)

    Google Scholar 

  31. Enze, Z.: Research on multi-objective particle swarm optimization algorithm and applications. Nanjing University of Science & Technology (2016)

    Google Scholar 

  32. Maoguo, G., Gang, C., Licheng, J.: Nondominated individual selection strategy based on adaptive partition for evolutionary multi-objective optimization. J. Comput. Res. Dev. 048(004), 545–557 (2011)

    Google Scholar 

  33. Han, X., Lei, H., Wang, Y.S.: Multiple paths test data generation based on particle swarm optimization. IET Softw. 11(2), 41–47 (2017)

    Google Scholar 

  34. Yao, X., Gong, D.: Genetic algorithm based test data generation for multiple paths via individual sharing. Comput. Intell. Neurosci. 2014(3), 59–70 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450000, China

    Wenning Zhang, Chongyang Jiao & Ting Xu

  2. Zhongyuan University of Technology, Zhengzhou, 450000, China

    Wenning Zhang

  3. Zhengzhou University, Zhengzhou, 450000, China

    Qinglei Zhou & Ting Xu

Authors
  1. Wenning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qinglei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chongyang Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenning Zhang .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. North University of China, Taiyuan, China

    Pinle Qin

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, W., Zhou, Q., Jiao, C., Xu, T. (2021). Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-5940-9_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5939-3

  • Online ISBN: 978-981-16-5940-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation