Skip to main content

Advertisement

SpringerLink
Log in

A novel hybrid approach for feature selection in software product lines

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Software Product Line (SPL) customizes software by combining various existing features of the software with multiple variants. The main challenge is selecting valid features considering the constraints of the feature model. To solve this challenge, a hybrid approach is proposed to optimize the feature selection problem in software product lines. The Hybrid approach ‘Hyper-PSOBBO’ is a combination of Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO) and hyper-heuristic algorithms. The proposed algorithm has been compared with Bird Swarm Algorithm (BSA), PSO, BBO, Firefly, Genetic Algorithm (GA) and Hyper-heuristic. All these algorithms are performed in a set of 10 feature models that vary from a small set of 100 to a high-quality data set of 5000. The detailed empirical analysis in terms of performance has been carried out on these feature models. The results of the study indicate that the performance of the proposed method is higher to other state-of-the-art algorithms.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ababneh J (2015) Greedy particle swarm and biogeography-based optimization algorithm. International Journal of Intelligent Computing and Cybernetics 8(1):28–49

    Article  MathSciNet  Google Scholar 

  2. Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415

    Article  MathSciNet  Google Scholar 

  3. Chen X, Tianfield H, Du W, Liu G (2016) Biogeography-based optimization with covariance matrix based migration. Appl Soft Comput 45:71–85

    Article  Google Scholar 

  4. Chhikara R, Kumari AC (2020) Feature selection optimization of HealthCare software product line using BBO. Procedia Computer Science 167:1696–1704

    Article  Google Scholar 

  5. Chhikara RR, Sharma P, Singh L (2016) A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis. Int J Mach Learn Cybern 7(6):1195–1206

    Article  Google Scholar 

  6. Clements P, Northrop L (2001) Software product lines: practices and patterns, Addison-Wesley Professional, Boston

  7. Cowling P, Kendall G, Soubeiga E (2001) Hyper heuristic Approach to Scheduling a Sales Summit. In: Proceedings of the Third International Conference of Practice And Theory of Automated Timetabling: 176–190

  8. Ding J, Hao K, Hou H (2011) The research on measurement and management of core asset library. In: 2011 2nd international conference on artificial intelligence. Management science and electronic commerce. IEEE: 3542–3545

  9. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the sixth international symposium on micro machine and human science. IEEE: 39–43

  10. Feng Q, Liu S, Tang G, Yong L, Zhang J (2013) Biogeography-based optimization with orthogonal crossover. Math Probl Eng 2013:1–20

    Google Scholar 

  11. Goodarzi M, dos Santos CL (2014) Firefly as a novel swarm intelligence variable selection method in spectroscopy. Anal Chim Acta 852:20–27

    Article  Google Scholar 

  12. Guo J, White J, Wang G, Li J, Wang Y (2011) A genetic algorithm for optimized feature selection with resource constraints in software product lines. J Syst Softw 84(12):2208–2221

    Article  Google Scholar 

  13. Kashyap N, Kumari AC (2018) Hyper-heuristic approach for service composition in internet of things. Electronic Government, an International Journal 14(4):321–339

    Article  Google Scholar 

  14. Kumari AC (2018) Feature selection optimization in SPL using genetic algorithm. Procedia computer science 132:1477–1486

    Article  Google Scholar 

  15. Kumari AC, Srinivas K (2016) Hyper-heuristic approach for multi-objective software module clustering. J Syst Softw 117:384–401

    Article  Google Scholar 

  16. Li J, Liu X, Wang Y, & Guo J (2011) Formalizing feature selection problem in software product lines using 0-1 programming. In Practical Applications of Intelligent Systems Springer: 459–465

  17. Lohokare MR, Panigrahi BK, Pattnaik SS, Devi S, Mohapatra A (2012) Neighborhood search-driven accelerated biogeography-based optimization for optimal load dispatch. IEEE Trans Syst Man Cybern Part C Appl Rev 42(5):641–652

    Article  Google Scholar 

  18. Ma H (2010) An analysis of the equilibrium of migration models for biogeography-based optimization. Inf Sci 180(18):3444–3464

    Article  Google Scholar 

  19. Ma H, Fei M, Ding Z, Jin J (2012) Biogeography-based optimization with ensemble of migration models for global numerical optimization. In: 2012 IEEE congress on evolutionary computation. IEEE: 1–8

  20. Ma H, Simon D, Fei M, Xie Z (2013) Variations of biogeography-based optimization and Markov analysis. Inf Sci 220:492–506

    Article  Google Scholar 

  21. Niu B, Li L (2008). A novel PSO-DE-based hybrid algorithm for global optimization. In: international conference on intelligent computing, springer :156-163

  22. Panchal VK, Singh P, Kaur N, Kundra H (2009) Biogeography based satellite image classification. 6(2):269–274

  23. Ping Z, Ping W, Chun F, Hong-yang Y (2013) A hybrid biogeography-based optimization with simplex method and its application. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering 32(2):575–585

    Article  MathSciNet  Google Scholar 

  24. Rarick R, Simon D, Villaseca FE, Vyakaranam B (2009) Biogeography-based optimization and the solution of the power flow problem. In: 2009 IEEE international conference on systems, man and cybernetics. IEEE: 1003-1008

  25. Robinson J, Sinton S, Rahmat-Samii Y (2002) Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: IEEE antennas and propagation society international symposium (IEEE cat. No. 02CH37313) IEEE: 314–317

  26. Silva MA, Coelho LD, Freire RZ (2010). Biogeography-based optimization approach based on predator-prey concepts applied to path planning of 3-DOF robot manipulator. In: 2010 IEEE 15th conference on emerging technologies & factory automation. IEEE: 1–8

  27. Simon D (2008) Biogeography-based optimization. IEEE transactions on evolutionary computation. (6):702–13

  28. Tsafarakis S, Marinakis Y, Matsatsinis N (2011) Particle swarm optimization for optimal product line design. Int J Res Mark 28(1):13–22

    Article  Google Scholar 

  29. Xian-Bing Meng, X.Z. Gao, Lihua Lu, Yu Liu & Hengzhen Zhang (2015) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):673–687

  30. Yadav H, Kumari AC (2018) Analysis of features using feature model in software product line: a case study. Int J Educ Manag 8(2):48–57

    Google Scholar 

  31. Yadav H, Kumari AC, Chhikara R (2020) Feature selection optimisation of software product line using metaheuristic techniques. Int J Embed Syst 13(1):50–64

    Article  Google Scholar 

  32. Yang XS (2010) Nature-inspired metaheuristic algorithms. Lniver Press, United Kingdom

  33. Zhu W, Duan H (2014) Chaotic predator–prey biogeography-based optimization approach for UCAV path planning. Aerosp Sci Technol 32(1):153–161

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The NorthCap University, Gurugram, Haryana, India

    Hitesh Yadav & Rita Chhikara

  2. Dayalbagh Educational Institute, Gurugram, Haryana, India

    A. Charan Kumari

Authors
  1. Hitesh Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rita Chhikara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Charan Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hitesh Yadav.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, H., Chhikara, R. & Kumari, A.C. A novel hybrid approach for feature selection in software product lines. Multimed Tools Appl 80, 4919–4942 (2021). https://doi.org/10.1007/s11042-020-09956-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09956-6

Keywords

Search

Navigation