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Dual transfer learning with generative filtering model for multiobjective multitasking optimization

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Abstract

Multiobjective multitasking optimization (MTO) has attracted more and more attention because of its ability to solve multiple multiobjective optimization problems simultaneously. By transferring knowledge between tasks, MTO can improve the performance of optimization tasks. However, if the way of knowledge transfer is not reasonable, it will have a negative impact on the performance of tasks. To solve this problem and ensure the effectiveness of knowledge transfer, this paper proposes a multiobjective evolutionary multitasking algorithm based on dual transfer learning with generative filtering model namely EMT–DLGM. Specifically, a dual transfer learning mechanism is proposed to reduce the difference between tasks and improve the efficiency of knowledge transfer through the global and local transfer strategies. Moreover, the generative filtering model is designed to generate promising solutions according to the multiple differential evolution operations and filtering model. The experimental results on three MTO test suites demonstrate that EMT–DLGM is superior or comparable to other state-of-the-art multiobjective evolutionary multitasking algorithms.

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References

  1. Gupta A, Ong YS, Feng L, Tan KC (2016) Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans Cybernet 47(7):1652–1665

    Article  Google Scholar 

  2. Zhou Z, Ma X, Liang Z, Zhu Z (2020) “Multi-objective multi-factorial memetic algorithm based on bone route and large neighborhood local search for VRPTW, In: IEEE Congress on Evolutionary Computation, pp. 1–8

  3. Min ATW, Ong YS, Gupta A, Goh CK (2017) Multiproblem surrogates: Transfer evolutionary multiobjective optimization of computationally expensive problems. IEEE Trans Evol Comput 23(1):15–28

    Article  Google Scholar 

  4. Yang C, Ding J, Jin Y, Wang C, Chai T (2018) Multitasking multiobjective evolutionary operational indices optimization of beneficiation processes. IEEE Trans Autom Sci Eng 16(3):1046–1057

    Article  Google Scholar 

  5. Wang Z, Wang X (2019) Multiobjective multifactorial operation optimization for continuous annealing production process. Ind Eng Chem Res 58(41):19166–19178

    Article  Google Scholar 

  6. Liu J, Li P, Wang G, Zha Y, Peng J, Xu G (2020) A multitasking electric power dispatch approach with multi-objective multifactorial optimization algorithm. IEEE Access 8:155902–155911

    Article  Google Scholar 

  7. Gupta A, Ong YS, Feng L (2015) Multifactorial evolution: toward evolutionary multitasking. IEEE Trans Evol Comput 20(3):343–357

    Article  Google Scholar 

  8. Yao S, Dong Z, Wang X, Ren L (2020) A multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy. Inf Sci 511:18–35

    Article  MathSciNet  MATH  Google Scholar 

  9. Lin J, Liu HL, Xue B, Zhang M, Gu F (2019) Multiobjective multitasking optimization based on incremental learning. IEEE Trans Evol Comput 24(5):824–838

    Article  Google Scholar 

  10. Chen Y, Zhong J, Feng L, Zhang J (2019) An adaptive archive-based evolutionary framework for many-task optimization. IEEE Trans Emerg Top Comput Intelligence 4(3):369–384

    Article  Google Scholar 

  11. Ding J, Yang C, Jin Y, Chai T (2017) Generalized multitasking for evolutionary optimization of expensive problems. IEEE Trans Evol Comput 23(1):44–58

    Article  Google Scholar 

  12. Bali KK, Gupta A, Feng L, Ong YS, Siew TP (2017) “Linearized domain adaptation in evolutionary multitasking”, In: IEEE Congress on Evolutionary Computation, pp. 1295–1302

  13. Feng L, Zhou L, Zhong J, Gupta A, Ong YS, Tan KC, Qin AK (2018) Evolutionary multitasking via explicit autoencoding. IEEE Trans Cybernet 49(9):3457–3470

    Article  Google Scholar 

  14. Liang Z, Dong H, Liu C, Liang W, Zhu Z (2020) “Evolutionary multitasking for multiobjective optimization with subspace alignment and adaptive differential evolution”, IEEE Transactions on Cybernetics

  15. Lin J, Liu HL, Tan KC, Gu F (2020) An effective knowledge transfer approach for multiobjective multitasking optimization. IEEE Trans Cybernet 51(6):3238–3248

    Article  Google Scholar 

  16. Zhou L, Feng L, Gupta A, Ong YS (2021) Learnable evolutionary search across heterogeneous problems via kernelized autoencoding. IEEE Trans Evol Comput 25(3):567–581

    Article  Google Scholar 

  17. Lim R, Gupta A, Ong YS, Feng L, Zhang AN (2021) Non-linear domain adaptation in transfer evolutionary optimization. Cogn Comput 13(2):290–307

    Article  Google Scholar 

  18. Gao W, Cheng J, Gong M, Li H, Xie J (2021) “Multiobjective multitasking optimization with subspace distribution alignment and decision variable transfer”, IEEE Transactions on Emerging Topics in Computational Intelligence

  19. Xue X, Zhang K, Tan KC, Feng L, Wang J, Chen G, Zhao X, Zhang L, Yao J (2020) “Affine transformation-enhanced multifactorial optimization for heterogeneous problems”, IEEE Transactions on Cybernetics

  20. Bali KK, Ong YS, Gupta A, Tan PS (2019) Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II. IEEE Trans Evol Comput 24(1):69–83

    Article  Google Scholar 

  21. Yang C, Ding J, Tan KC, Jin Y (2017) “Two-stage assortative mating for multi-objective multifactorial evolutionary optimization”, In: IEEE 56th Annual Conference on Decision and Control, pp. 76–81

  22. Dang Q, Gao W, Gong M (2022) Multiobjective multitasking optimization assisted by multidirectional prediction method. Complex Intell Syst 8(2):1663–1679

    Article  Google Scholar 

  23. Tuan NQ, Hoang TD, Binh HTT (2018) “A guided differential evolutionary multi-tasking with powell search method for solving multi-objective continuous optimization”, In: IEEE Congress on Evolutionary Computation, pp. 1–8

  24. Xu Z, Zhang K, Xu X, He J (2019) A fireworks algorithm based on transfer spark for evolutionary multitasking. Front Neurorobot 13:109–109

    Article  Google Scholar 

  25. Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  26. Pan SJ, Tsang IW, Kwok JT, Yang Q (2010) Domain adaptation via transfer component analysis. IEEE Trans Neural Networks 22(2):199–210

    Article  Google Scholar 

  27. Wang Y, Xia Y, Zhao L, Bian J, Qin T, Liu G, Liu TY (2018) “Dual transfer learning for neural machine translation with marginal distribution regularization”, In: Proceedings of the AAAI Conference on Artificial Intelligence

  28. Sun B, Saenko K (2015) “Subspace distribution alignment for unsupervised domain adaptation”, In: Proceedings Brithsh Machine Vision conference, pp. 1–10

  29. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  30. Aljundi R, Emonet R, Muselet D, Sebban M (2015) “Landmarks-based kernelized subspace alignment for unsupervised domain adaptation”, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 56–63

  31. Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32–40

    Article  MathSciNet  MATH  Google Scholar 

  32. Zhou L, Zhou A, Zhang G, Shi C (2011) “An estimation of distribution algorithm based on nonparametric density estimation”, In: IEEE Congress of Evolutionary Computation, pp. 1597-1604

  33. Gong W, Zhou A, Cai Z (2015) A multioperator search strategy based on cheap surrogate models for evolutionary optimization. IEEE Trans Evol Comput 19(5):746–758

    Article  Google Scholar 

  34. Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution-an updated survey. Swarm Evol Comput 27:1–30

  35. Yuan Y, Ong YS, Feng L, Qin AK, Gupta A, Da B, Zhang Q, Tan KC, Jin Y, Ishibuchi H (2017) “Evolutionary multitasking for multiobjective continuous optimization: Benchmark problems, performance metrics and baseline results”, arXiv preprint arXiv:1706.02766

  36. Feng L, Qin K, Gupta A, Yuan Y, Ong YS, Chi X (2019). IEEE CEC 2019 Competition on Evolutionary Multi-Task Optimization. [Online]. Available: http://cec2019.org/programs/competitions.html#cec02

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

    Article  Google Scholar 

  38. Bali KK, Gupta A, Ong YS, Tan PS (2020) Cognizant multitasking in multiobjective multifactorial evolution: MO-MFEA-II. IEEE Trans Cybernet 51(4):1784–1796

    Article  Google Scholar 

  39. Liang Z, Liang W, Wang Z, Ma X, Liu L, Zhu Z (2021) “Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution”, IEEE Transactions on Systems, Man, and Cybernetics: Systems

  40. Van Veldhuizen DA, Lamont GB (1998) “Multiobjective evolutionary algorithm research: A history and analysis”, Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio

  41. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9(2):115–148

    MathSciNet  MATH  Google Scholar 

  42. Zitzler E, Kunzli S (2004) “Indicator-based selection in multiobjective search”, In: International Conference on Parallel Problem Solving from Nature, pp. 832–842

  43. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Publ Am Stat Assoc 32(200):675–701

    Article  MATH  Google Scholar 

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Grant 61772391 and 62106186, in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2022JQ–670, in part by the Fundamental Research Funds for the Central Universities under Grant YJS2215.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710126, People’s Republic of China

    Qianlong Dang & Weifeng Gao

  2. Key Laboratory of Intelligent Perception and Image Understanding, International Research Center for Intelligent Perception and Computation, Ministry of Education, Xidian University, Xi’an, 710071, People’s Republic of China

    Maoguo Gong

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  1. Qianlong Dang
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  2. Weifeng Gao
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  3. Maoguo Gong
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Correspondence to Weifeng Gao.

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Dang, Q., Gao, W. & Gong, M. Dual transfer learning with generative filtering model for multiobjective multitasking optimization. Memetic Comp. 15, 3–29 (2023). https://doi.org/10.1007/s12293-022-00374-9

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