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A multiobjective evolutionary algorithm based on surrogate individual selection mechanism

  • Xiaoji Chen
  • Bin WuEmail author
  • Pengcheng Sheng
Original Article

Abstract

Recently, classification-based preselection (CPS) strategy for evolutionary multiobjective optimization has been found to be very effective and efficient for solving complicated multiobjective optimization problems (MOPs). However, this strategy can only classify the candidate solutions into different categories, but it is difficult to find out which one is the best. In order to overcome this shortcoming, we propose a surrogate individual selection mechanism for multiobjective evolutionary algorithm based on decomposition. In this mechanism, we get the best one from candidate solution set by surrogate model, which mitigates the risk of using CPS strategy. Furthermore, we generate candidate solution set through a new offspring generation strategy, which can improve the quality of the candidate solutions. Based on typical multiobjective evolutionary algorithm MOEA/D, we design a new algorithm framework, called MOEA/D-SISM, through integrating the proposed surrogate individual selection mechanism. We compare MOEA/D-SISM with other state-of-the-art multiobjective evolutionary algorithms (MOEAs), and experimental results show that our proposed algorithm obtains the best performance.

Keywords

Multiobjective optimization MOEA/D Surrogate Preselection 

Notes

Funding information

This research is supported in part by National Key Research and Development Program “New Energy Vehicle” Key Special Project Subsidy, Project Name: “Research and Development of Electronic and Electrical Architecture of Intelligent Electric Vehicle”, Project No. 2017YFB0102500. Xingtai City Science and Technology Bureau, Project Name: Research on obstacle avoidance method of autonomous intelligent electric vehicle in complex environment, Project No.2018ZC022.

References

  1. 1.
    Brys T, Harutyunyan A, Vrancx P, Now A, Taylor ME (2017) Multiobjectivization and ensembles of shapings in reinforcement learning [J]. Neurocomputing 263:48–59Google Scholar
  2. 2.
    Lin Q, Liu Z, Yan Q, Du Z, Coello CAC, Liang Z, Wang W, Chen J (2016) Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm [J]. Inf Sci 339:332–352Google Scholar
  3. 3.
    Shi C, Kong X, Fu D, Yu PS, Wu B (2014) Multi-label classification based on multi-objective optimization [J]. ACM Trans Intell Syst Technol 5(2):1–22Google Scholar
  4. 4.
    Liu J, Gong M, Miao Q, Wang X, Li H, Liu J, Gong M, Miao Q (2018) Structure learning for deep neural networks based on multiobjective optimization [J]. IEEE Trans Neural Netwo Learn Syst 29(6):2450–2463MathSciNetGoogle Scholar
  5. 5.
    Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art [J]. Swarm Evol Comput 1(1):32–49Google Scholar
  6. 6.
    Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms [M]. MIT Press, CambridgeGoogle Scholar
  7. 7.
    Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: Nsga-II [C]. In: International conference on parallel problem solving from nature, pp 849–858Google Scholar
  8. 8.
    Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms a comparative case study [M]. Springer, Berlin HeidelbergGoogle Scholar
  9. 9.
    Laumanns M (2001) Spea2 : improving the strength pareto evolutionary algorithm [C]. In: Technical report GloriastrasseGoogle Scholar
  10. 10.
    Zitzler E, Knzli S (2004) Indicator-based selection in multiobjective search [C]. Lect Notes Comput Sci 3242:832–842Google Scholar
  11. 11.
    Basseur M, Zitzler E (2008) A preliminary study on handling uncertainty in indicator-based multiobjective optimization [J]. Lect Notes Comput Sci 2(3):727–739Google Scholar
  12. 12.
    Bader J, Zitzler E (2014) Hype: an algorithm for fast hypervolume-based many-objective optimization [C]. Evol Comput 19(1):45–76Google Scholar
  13. 13.
    Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition [J]. IEEE Trans Evol Comput 11(6):712–731Google Scholar
  14. 14.
    Li H, Zhang Q (2009) Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii [J]. IEEE Trans Evol Comput 13(2):284–302Google Scholar
  15. 15.
    Zhang Q, Liu W, Li H (2009) The performance of a new version of moea/d on cec09 unconstrained mop test instances [C]. In: IEEE Congress on Evolutionary Computation. IEEE Press, Piscataway, pp 203–208Google Scholar
  16. 16.
    Mashwani WK, Salhi A (2012) A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation [J]. Appl Soft Comput 12(9):2765–2780Google Scholar
  17. 17.
    Zhou A, Zhang Q, Zhang G (2012) A multiobjective evolutionary algorithm based on decomposition and probability model [C]. IEEE Trans Evol Comput:1–8.  https://doi.org/10.1109/CEC.2012.6252954
  18. 18.
    Zhang H, Zhou A, Zhang G, Singh HK (2017) Accelerating moea/d by nelder-mead method [C]. IEEE Trans Evol Comput:976–983.  https://doi.org/10.1109/CEC.2017.7969414
  19. 19.
    Zhang J, Zhou A, Zhang G (2015) A multiobjective evolutionary algorithm based on decomposition and preselection [J]. In: Bio-inspired computing - theories and applications, pp 631–642Google Scholar
  20. 20.
    Lin X, Zhang Q, Kwong S (2016) A decomposition based multiobjective evolutionary algorithm with classification [C]. Evol Comput:3292–3299Google Scholar
  21. 21.
    Zhao SZ, Suganthan PN, Zhang Q (2012) Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes [J]. IEEE Trans Evol Comput 16(3):442–446Google Scholar
  22. 22.
    Li K, Fialho A, Kwong S, Zhang Q (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition [J]. IEEE Trans Evol Comput 18(1):114–130Google Scholar
  23. 23.
    Venske SM, Gon RAA, Delgado MR (2014) Ademo/d: multiobjective optimization by an adaptive differential evolution algorithm [J]. Neurocomputing 127(127):65–77Google Scholar
  24. 24.
    Lin Q, Tang C, Ma Y, Du Z, Li J, Chen J, Ming Z (2017) A novel adaptive control strategy for decomposition-based multiobjective algorithm [J]. Comput Oper Res 78:94–107MathSciNetzbMATHGoogle Scholar
  25. 25.
    Li K, Zhang Q, Kwong S, Li M, Wang R (2014) Stable matching-based selection in evolutionary multiobjective optimization [J]. IEEE Trans Evol Comput 18(6):909–923Google Scholar
  26. 26.
    Li K, Kwong S, Zhang Q, Deb K (2015) Interrelationship-based selection for decomposition multiobjective optimization [J]. IEEE Trans Cybern 45(10):2076–2088Google Scholar
  27. 27.
    Chen X, Shi C, Zhou A, Wu B, Cai Z (2017) A decomposition based multiobjective evolutionary algorithm with semi-supervised classification[C]. IEEE Congress Evol Comput:797–804.  https://doi.org/10.1109/CEC.2017.7969391
  28. 28.
    Tan Y, Zhu Y (2010) Fireworks algorithm for optimization [C]. In: International conference on advances in swarm intelligence, pp 355–364Google Scholar
  29. 29.
    Naujoks B, Beume N, Emmerich M (2005) Multi-objective optimisation using s-metric selection: application to three-dimensional solution spaces [C]. Evol Comput 2:1282–1289zbMATHGoogle Scholar
  30. 30.
    Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art [J]. IEEE Trans Evol Comput 15(1):4–31Google Scholar
  31. 31.
    Deb K, Goyal M (1996) A combined genetic adaptive search (geneas) for engineering design [J]. Computer Science and Informatics 26(4):30–45Google Scholar
  32. 32.
    Hillermeier C (1999) Nonlinear multiobjective optimization [J]. J Oper Res Soc 51:246Google Scholar
  33. 33.
    Cai Z, Wang Y (2006) A multiobjective optimization-based evolutionary algorithm for constrained optimization [J]. IEEE Trans Evol Comput 10(6):658–675Google Scholar
  34. 34.
    Vapnik VN (1998) Statistical learning theory [M]. Encyclopedia of the sciences of. Learning 41(4):3185–3185Google Scholar
  35. 35.
    Wang Z, Zhang Q, Zhou A, Gong M, Jiao L (2017) Adaptive replacement strategies for moea/d [J]. IEEE Trans Cybern 46(2):474–486Google Scholar
  36. 36.
    Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation [J]. Soft Comput 9(1):3–12Google Scholar
  37. 37.
    You H, Yang M, Wang D, Jia X (2009) Kriging model combined with latin hypercube sampling for surrogate modeling of analog integrated circuit performance [C]. The 10th International Symposium on Quality Electronic Desig:554–558.  https://doi.org/10.1109/ISQED.2009.4810354
  38. 38.
    Yu C, Kelley L, Zheng S, Tan Y (2014) Fireworks algorithm with differential mutation for solving the cec 2014 competition problems [C]. In IEEE Congress on Evolutionary Computation. IEEE Press, Piscataway, pp 3238–3245Google Scholar
  39. 39.
    Tan Y (2015) S-metric-based multi-objective fireworks algorithm. IEEE Trans Evol Comput [C]:1257–1264.  https://doi.org/10.1109/CEC.2015.7257033
  40. 40.
    Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters [J]. IEEE Trans Evol Comput 15(1):55–66Google Scholar
  41. 41.
    Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2008) Multiobjective optimization test instances for the cec 2009 special session and competition [M]. University of Essex, ColchesterGoogle Scholar
  42. 42.
    Li Y, Zhou A, Zhang G (2014) An moea/d with multiple differential evolution mutation operators [C]. IEEE Trans Evol Comput :397–404.  https://doi.org/10.1109/CEC.2014.6900339
  43. 43.
    Fonseca CM, Knowles JD, Thiele L, Zitzler E (2005) A tutorial on the performance assessment of stochastic multiobjective optimizers [C]. The third international conference on evolutionary multi-criterion. Optimization 216:240Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Key Lab of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of Mechanical EngineeringHebei University of TechnologyTianjinChina
  3. 3.Automotive Engineering Department, Xingtai Polytechnic CollegeXingtaiChina

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