Skip to main content
SpringerLink
Log in

Mobile Handset Selection Using Evolutionary Multi-objective Optimization Considering the Cost and Quality Parameters

  • Conference paper
  • First Online:
Advances in Computing and Data Sciences (ICACDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 906))

Included in the following conference series:

Abstract

Multi-objective optimization is a mathematical framework to deal with conflicting objectives simultaneously. Evolutionary algorithms are extremely useful in implementing multi-objective optimization problems resulting into a new research area named Evolutionary Multi-objective Optimization (EMO). This paper also implements the problem of mobile handset selection considering two objectives which are conflicting, cost and quality of the handset using EMO. The problem is implemented using ‘gamultobj’ solver available in Matlab ‘optimization’ toolbox. The results are shown using Pareto Front at different number of generations.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Neustadt, L.W.: Optimization: A Theory of Necessary Conditions. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  2. Liu, H.-L., Gu, F., Zhang, Q.: Decomposition of multi-objective optimization problem into number of simple multiobjective subproblems. IEEE Trans. Evol. Comput. 18(3), 450–455 (2013)

    Article  Google Scholar 

  3. Zhang, X., Tian, Y., Cheng, R., Jin, Y.: An efficient approach to non dominated sorting for evolutionary multi objective optimization. IEEE Trans. Evol. Comput. 19(2), 201–213 (2014)

    Article  Google Scholar 

  4. Shukla, P.K., Tripathi, S.P.: A survey on interpretability–accuracy (I–A) trade-off in evolutionary fuzzy Systems. In: IEEE International Conference on Genetic and Evolutionary Computation (ICGEC 2011), Japan, 29 August–1 September (2011)

    Google Scholar 

  5. Shukla, P.K., Tripathi, S.P.: A review on the interpretability-accuracy trade-off in evolutionary multi-objective fuzzy systems (EMOFS). Information 3, 256–277 (2012)

    Article  Google Scholar 

  6. Shukla, P.K., Tripathi, S.P.: Handling high dimensionality and interpretability accuracy trade-off issues in evolutionary multi-objective fuzzy classifiers. Int. J. Sci. Eng. Res. 5(6), 665–671 (2014)

    Google Scholar 

  7. Shukla, P.K., Tripathi, S.P.: Interpretability and accuracy issues in evolutionary multi-objective fuzzy classifiers. Int. J. Soft Comput. Netw. 1(1), 55–69 (2016)

    Article  Google Scholar 

  8. Shukla, P.K., Tripathi, S.P.: On the design of interpretable evolutionary fuzzy system (I-EFS) with improved accuracy. In: International Conference on Computing Science, L. P. University, India (2012)

    Google Scholar 

  9. Shukla, P.K., Tripathi, S.P.: Interpretability issues evolutionary multi objective fuzzy knowledge base systems. In: 7th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-7A2012), ABV-IIIT, Gwalior, India, 14–16 December (2012)

    Google Scholar 

  10. Abratiam, A., Jain, L., Goldberg, R. (eds.): Evolutionary Multi-objective Optimization: Theoretical Advances and Applications. Springer, Berlin (2005). https://doi.org/10.1007/1-84628-137-7

    Book  Google Scholar 

  11. Burkart, R.M., Kolar, J.W.: Comparative life cycle cost analysis of Si and SiCPV converter systems based on advanced n-p-6 multi-objective optimization technique. IEEE Trans. Power Electron. 32(6), 4344–4358 (2017)

    Article  Google Scholar 

  12. G–Bediaga, A., Viller, I., Rujas, A., Nir, L., Rfurer, A.: Multi-objective optimization of medium frequency transformers for isolated soft-switching converters using a genetic algorithm. IEEE Trans. Power Electron. 32(4), 2995–3006 (2017)

    Article  Google Scholar 

  13. Shabestary, A.-R., Mohamed, I.: Analytical expressions for multi-objective optimization of converter-based DG operation under unbalanced grid conditions. IEEE Trans. Power Electron. 32(9), 7284–7296 (2017)

    Article  Google Scholar 

  14. Zaman, M., Rangaiah, G.P.: Multi-objective optimization application in chemical engineering. In: Multi-objective Optimization, pp. 29–62 (2017)

    Chapter  Google Scholar 

  15. Wang, Y., Li, Y., Jiao, L.: Quantum-inspired multi-objective optimization evolutionary algorithm based on decomposition. Soft Comput. 20(8), 3257–3272 (2016)

    Article  Google Scholar 

  16. Perera, A.T.D., Sirinanna, M.P.G., Attalage, R.A., Perera, K.C.K., Dassanaake, V.P.C.: Multi objective optimization and multi criterion decision making in expanding existing standalone energy system combining renewable energy sources. In: Proceedings of Engineering and Applied Science (2012). https://doi.org/10.2316/P.2012.785-108

  17. Esfe, M.H., Hajmohammad, H., Toghraie, D., Rostanian, H., Mahaian, O., Wongisses, S.: Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems. Energy 137, 160–171 (2017)

    Article  Google Scholar 

  18. Pastori, M., Udias, A., Bouraoui, F., Biodoglio, G.: A multi-objective approach to evaluate the economic and environmental impacts of alternative water and nutrient management strategies in Africa. J. Environ. Inform. 29(1), 193–201 (2017)

    Google Scholar 

  19. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, MA (1989)

    MATH  Google Scholar 

  20. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  21. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, pp. 82–87, Piscataway, New Jersey, June 1994. IEEE Service Center (1994)

    Google Scholar 

  22. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest, S. (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423, San Mateo, California. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers (1993)

    Google Scholar 

  23. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative study. In: Eiben, A.E. (ed.) Parallel Problem Solving from Nature V, pp. 292–301. Springer, Amsterdam (1998). https://doi.org/10.1007/BFb0056872

    Chapter  Google Scholar 

  24. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  25. 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 

  26. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: Giannakoglou, K., et al. (eds.) EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100, Athens, Greece (2002)

    Google Scholar 

  27. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T., et al.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M. (ed.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_83

    Chapter  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, India

    Anurag Tiwari

  2. Department of Computer Science and Engineering, Babu Banarasi Das Northern India Institute of Technology, Lucknow, India

    Vivek Kumar Singh

  3. Department of Information Technology, Babu Banarasi Das Northern India Institute of Technology, Lucknow, India

    Praveen Kumar Shukla

Authors
  1. Anurag Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Praveen Kumar Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anurag Tiwari .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague 8, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, Canada

    Tuncer Ören

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tiwari, A., Singh, V.K., Shukla, P.K. (2018). Mobile Handset Selection Using Evolutionary Multi-objective Optimization Considering the Cost and Quality Parameters. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1813-9_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1812-2

  • Online ISBN: 978-981-13-1813-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation