Skip to main content
SpringerLink
Log in

Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art

  • Conference paper
Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

Abstract

Many optimization problems involve multiple objectives, constraints and parameters that change over time. These problems are called dynamic multiobjective optimization problems (DMOPs) and have recently attracted a lot of research. In this chapter, we provide a survey of the state-of-the-art on the field of dynamic multi-objective optimization with regards to the definition and classification of DMOPS, test problems, performance measures and optimization approaches. We provide a comprehensive definition of DMOPs and identify gaps, challenges and future works in dynamic multi-objective optimization.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proc. 1999 IEEE Congr. Evol. Comput., pp. 1875–1882 (2005)

    Google Scholar 

  2. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers (2001)

    Google Scholar 

  3. Branke, J., Kauler, T., Schmidth, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Proc. 4th Int. Conf. Adaptive Comput. Des. Manuf., pp. 299–308 (2000)

    Google Scholar 

  4. Bui, L., Abbass, H., Branke, J.: Multiobjective optimization for dynamic environments. In: Proc. 2005 IEEE Congr. Evol. Comput., pp. 2349–2356 (2005)

    Google Scholar 

  5. Cámara, M., Ortega, J., de Toro, F.: Performance measures for dynamic multi-objective optimization. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009, Part I. LNCS, vol. 5517, pp. 760–767. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Cobb, H.: An Investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report, Naval Research Laboratory (1990)

    Google Scholar 

  7. Dasgupta, D., Mcgregor, D.: Nonstationary function 0ptimization using the structured genetic algorithm. In: Proc. 2nd Int. Conf. Parallel Problem Solving from Nature, pp. 145–154 (1992)

    Google Scholar 

  8. Deb, K., Rao N., U.B., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans. Evol. Comput. 8(5), 425–442 (2004)

    Article  Google Scholar 

  10. Goh, C., Tan, K.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 13(1), 103–127 (2009)

    Article  Google Scholar 

  11. Goh, C.-K., Tan, K.C.: Evolutionary Multi-objective Optimization in Uncertain Environments. SCI, vol. 186. Springer, Heidelberg (2009)

    MATH  Google Scholar 

  12. Goldberg, D., Smith, R.: Nonstationary function optimization using genetic algorithm with dominance and diploidy. In: Proc. 2nd Int. Conf. Genetic Algorithms and Their Applications, pp. 59–68 (1987)

    Google Scholar 

  13. Greeff, M., Engelbrecht, A.: Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation. In: Proc. 2008 IEEE Congr. Evol. Comput., pp. 2917–2924 (2008)

    Google Scholar 

  14. Grefenstette, J.: Genetic algorithms for changing environments. In: Proc. Int. Conf. Parallel Problem Solving from Nature, pp. 137–144 (1992)

    Google Scholar 

  15. Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proc. 8th Annual Conf. Genetic and Evol. Comput., pp. 1201–1208 (2006)

    Google Scholar 

  16. Helbig, M., Engelbrecht, A.: Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. In: Proc. 2011 IEEE Congr. Evol. Comput., pp. 2047–2054 (2011)

    Google Scholar 

  17. Huang, L., Suh, I., Abraham, A.: Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants. Inf. Sci. 181(11), 2370–2391 (2011)

    Article  Google Scholar 

  18. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)

    Article  Google Scholar 

  19. Jin, Y., Sendhoff, B.: Constructing dynamic optimization test problems using the multi-objective optimization concept. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 525–536. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  20. Koo, W., Goh, C., Tan, K.: A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment. Memetic Computing 2, 87–110 (2010)

    Article  Google Scholar 

  21. Li, X., Branke, J., Kirley, M.: On performance metrics and particle swarm methods for dynamic multiobjective optimization problems. In: Proc. 2007 IEEE Congr. Evol. Comput., pp. 576–583 (2007)

    Google Scholar 

  22. Liu, C.: New dynamic multiobjective evolutionary algorithm with core estimation of distribution. In: Proc. 2010 Int. Conf. Electrical and Control Engineering, pp. 1345–1348 (2010)

    Google Scholar 

  23. Mehnen, J., Wagner, T., Rudolph, G.: Evolutionary optimization of dynamic multi-objective test functions. In: Proc. 2nd Italian Workshop on Evol. Comput. (2006)

    Google Scholar 

  24. Morrison, R.: Designing evolutionary algorithms for dynamic environments. Springer (2004)

    Google Scholar 

  25. Ng, K., Wong, K.: A wew diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proc. 6th Int. Conf. Genetic Algorithms, pp. 159–166 (1995)

    Google Scholar 

  26. Nguyen, T.: Continuous dynamic optimisation using evolutionary algorithms. PhD Thesis, University of Birmingham (2011)

    Google Scholar 

  27. Ramsey, C., Grefenstette, J.: Case-based initialization of genetic algorithms. In: Proc. 5th Int. Conf. Genetic Algorithms, pp. 84–91 (1993)

    Google Scholar 

  28. Schaffer, J.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proc. 1st Int. Conf. on Genetic Algorithms, pp. 93–100 (1985)

    Google Scholar 

  29. Tantar, A., Tantar, E., Bouvry, P.: A classification of dynamic multi-objective optimization problems. In: Proc. 13th Annual Conf. Genetic and Evol. Comput., pp. 105–106 (2011)

    Google Scholar 

  30. Van Veldhuizen, D.: Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. PhD Thesis, Air Force Institute of Technology (1999)

    Google Scholar 

  31. Vavak, F., Jukes, K., Fogarty, T.: Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search. In: Proc. 7th Int. Conf. Genetic Algorithms, pp. 719–726 (1997)

    Google Scholar 

  32. Weicker, K.: Performance measures for dynamic environments. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 64–73. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  33. Yang, S.: Associative memory scheme for genetic algorithms in dynamic environments. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 788–799. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  34. Zeng, S., Chen, G., Zheng, L., Shi, H., de Garis, H., Ding, L., Kang, L.: A dynamic multi-objective evolutionary algorithm based on an orthogonal design. In: Proc. 2006 IEEE Congr. Evol. Comput., pp. 573–580 (2006)

    Google Scholar 

  35. Zhang, Z.: Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Appl. Soft Comput. 8(2), 959–971 (2008)

    Article  Google Scholar 

  36. Zhang, Z., Qian, S.: Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft Comput. 15(7), 1333–1349 (2011)

    Article  Google Scholar 

  37. Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 832–846. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  38. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  39. 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 

Download references

Author information

Authors and Affiliations

  1. Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, Birmingham, B15 2TT, U.K.

    Carlo Raquel & Xin Yao

Authors
  1. Carlo Raquel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlo Raquel .

Editor information

Editors and Affiliations

  1. , School of Computer Science and Informati, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  2. , School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Raquel, C., Yao, X. (2013). Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38416-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38415-8

  • Online ISBN: 978-3-642-38416-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation