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Knowledge Reconstruction for Dynamic Multi-objective Particle Swarm Optimization Using Fuzzy Neural Network

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Abstract

Many real−world applications are dynamic multi−objective optimization problems (DMOPs). The transfer of knowledge in the evolutionary process is believed to have advantages in solving DMOPs. However, most existing works can hardly be focused on the effectiveness of knowledge, which may lead to the negative transfer to degrade searching performance of the population. To address this issue, a knowledge reconstruction (KR) method is proposed for dynamic multi−objective particle swarm optimization (DMOPSO) using fuzzy neural network (FNN). The contributions of the proposed KR−DMOPSO are threefold: First, a knowledge extraction method, using a FNN model, is developed to obtain the domain knowledge of two successive Pareto optimal sets when dynamic occurs. Then, the domain knowledge can be applied to explore the evolutionary tendency. Second, a knowledge evaluation mechanism, based on the diversity and convergence of non−dominated solutions, is devised to select the domain knowledge. Then, the effective knowledge can be achieved. Third, a knowledge reconstruction strategy is designed to obtain the suitable domain knowledge. Then, this knowledge can be used to adapt to dynamic environments to improve the searching performance of the population. Finally, the proposed KR−DMOPSO is compared with other advanced dynamic multi−objective optimization algorithms (DMOAs). The results show that the proposed KR−DMOPSO is superior to other compared algorithms.

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References

  1. Shen, X., Minku, L.L., Bahsoon, R., Yao, X.: Dynamic software project scheduling through a proactive−rescheduling method. IEEE Trans. Softw. Eng. 42, 658–686 (2016)

    Article  Google Scholar 

  2. Zhou, H., Zeng, Z., Lian, L.: Adaptive re−planning of AUVs for environmental sampling missions: A fuzzy decision support system based on multi−objective particle swarm optimization. Int. J. Fuzzy Syst. 20, 650–671 (2018)

    Article  Google Scholar 

  3. Han, H.G., Liu, Z., Lu, W., Hou, Y., Qiao, J.F.: Dynamic MOPSO−based optimal control for wastewater treatment process. IEEE Trans. Cybern. 51, 2518–2528 (2021)

    Article  Google Scholar 

  4. Shahverdian, M.H., Sohani, A., Sayyaadi, H., Samiezadeh, S., Doranehgard, M.H., Karimi, N., Li, L.K.B.: A dynamic multi−objective optimization procedure for water cooling of a photovoltaic module. Sustain. Energy Technol. Assess. 45, 101111 (2021)

    Google Scholar 

  5. Li, H., Song, B., Tang, X., Xie, Y., Zhou, X.: Adaptive pareto optimal control of T−S fuzzy system with input constraints and its application. Int. J. Fuzzy Syst. (2021). https://doi.org/10.1007/s40815−021−01180−0

    Article  Google Scholar 

  6. Rizk−Allah, R.M., Abo−Sinna, M.A., Hassanien, A.E.: Intuitionistic fuzzy sets and dynamic programming for multi−objective non−linear programming problems. Int. J. Fuzzy Syst. 23, 334–352 (2021)

    Article  Google Scholar 

  7. Khosraviani, M., Jahanshahi, M., Farahani, M., Bidaki, A.R.Z.: Load−Frequency Control Using Multi−objective Genetic Algorithm and Hybrid Sliding Mode Control−Based SMES. Int. J. Fuzzy Syst. 20, 280–294 (2018)

    Article  MathSciNet  Google Scholar 

  8. Zhou, A., Jin, Y., Zhang, Q.: A Population prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Trans. Cybern. 44, 40–53 (2014)

    Article  Google Scholar 

  9. Ahrari, A., Elsayed, S., Sarker, R., Essam, D., CoelloCoello, C.A.: Weighted pointwise prediction method for dynamic multiobjective optimization. Inf. Sci. 546, 349–367 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  10. Wang, C., Yen, G.G., Zou, F.: A novel predictive method based on key points for dynamic multi−objective optimization. Expert Syst. Appl. (2022). https://doi.org/10.1016/j.eswa.2021.116127

    Article  Google Scholar 

  11. Rong, M., Gong, D., Pedrycz, W., Wang, L.: A multimodel prediction method for dynamic multiobjective evolutionary optimization. IEEE Trans. Evol. Comput. 24, 290–304 (2020)

    Article  Google Scholar 

  12. Wang, Y., Li, B.: Multi−strategy ensemble evolutionary algorithm for dynamic multi−objective optimization. Memetic Comput. 2, 3–24 (2010)

    Article  Google Scholar 

  13. Jiang, M., Qiu, L., Huang, Z., Yen, G.G.: Dynamic multi−objective estimation of distribution algorithm based on domain adaptation and nonparametric estimation. Inf. Sci. 435, 203–223 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  14. Wu, Y., Shi, L., Liu, X.: A new dynamic strategy for dynamic multi−objective optimization. Inf. Sci. 529, 116–131 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  15. Rong, M., Gong, D., Zhang, Y., Jin, Y., Pedrycz, W.: Multidirectional prediction approach for dynamic multiobjective optimization problems. IEEE Trans. Cybern. 49, 3362–3374 (2019)

    Article  Google Scholar 

  16. Jiang, S., Yang, S.: A steady−state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 21, 65–82 (2017)

    Article  Google Scholar 

  17. Ma, X., Yang, J., Sun, H., Hu, Z., Wei, L.: Multiregional co−evolutionary algorithm for dynamic multiobjective optimization. Inf. Sci. 545, 1–24 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hu, Y., Ou, J., Zheng, J., Zou, J., Yang, S., Ruan, G.: Solving dynamic multi−objective problems with an evolutionary multi−directional search approach. Knowledge−Based Syst. 194, 105175 (2020)

    Google Scholar 

  19. Azzouz, R., Bechikh, S., Said, L.: Ben: A dynamic multi−objective evolutionary algorithm using a change severity−based adaptive population management strategy. Soft Comput. 21, 885–906 (2017)

    Article  Google Scholar 

  20. Zhang, Q., Yang, S., Jiang, S., Wang, R., Li, X.: Novel prediction strategies for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 24, 260–274 (2020)

    Article  Google Scholar 

  21. Xu, B., Zhang, Y., Gong, D., Guo, Y., Rong, M.: Environment sensitivity−based cooperative co−evolutionary algorithms for dynamic multi−objective optimization. IEEE/ACM Trans. Comput. Biol. Bioinform. 15, 1877–1890 (2018)

    Article  Google Scholar 

  22. Wang, S., Liu, G., Gao, M., Cao, S., Guo, A., Wang, J.: Heterogeneous comprehensive learning and dynamic multi−swarm particle swarm optimizer with two mutation operators. Inf. Sci. 540, 175–201 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  23. Liu, R., Li, J., fan, J., Mu, C., Jiao, L.: A coevolutionary technique based on multi−swarm particle swarm optimization for dynamic multi−objective optimization. Eur. J. Oper. Res. 261, 1028–1051 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  24. Jiang, M., Wang, Z., Guo, S., Gao, X., Tan, K.C.: Individual−based transfer learning for dynamic multiobjective optimization. IEEE Trans. Cybern. 51, 4968–4981 (2021)

    Article  Google Scholar 

  25. Liu, X.F., Zhou, Y.R., Yu, X.: Cooperative particle swarm optimization with reference−point−based prediction strategy for dynamic multiobjective optimization. Appl. Soft Comput. J. 87, 105988 (2020)

    Article  Google Scholar 

  26. Zhang, S., Xie, J., Wang, H.: Fuzzy adaptive NSGA−III for large−scale optimization problems. Int. J. Fuzzy Syst. (2022). https://doi.org/10.1007/s40815−021−01220−9

    Article  Google Scholar 

  27. Yang, C., Ding, J., Jin, Y., Chai, T.: Offline data−driven multiobjective optimization: Knowledge transfer between surrogates and generation of final solutions. IEEE Trans. Evol. Comput. 24, 409–423 (2020)

    Google Scholar 

  28. Zou, J., Li, Q., Yang, S., Bai, H., Zheng, J.: A prediction strategy based on center points and knee points for evolutionary dynamic multi−objective optimization. Appl. Soft Comput. 61, 806–818 (2017)

    Article  Google Scholar 

  29. Jiang, M., Huang, Z., Qiu, L., Huang, W., Yen, G.G.: Transfer learning−based dynamic multiobjective optimization algorithms. IEEE Trans. Evol. Comput. 22, 501–514 (2018)

    Article  Google Scholar 

  30. Liefooghe, A., Daolio, F., Verel, S., Derbel, B., Aguirre, H., Tanaka, K.: Landscape−aware performance prediction for evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 24, 1063–1077 (2020)

    Article  Google Scholar 

  31. Liu, R., Li, J., Fan, J., Jiao, L.: A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction. Appl. Soft Comput. J. 73, 434–459 (2018)

    Article  Google Scholar 

  32. Cao, L., Xu, L., Goodman, E.D., Bao, C., Zhu, S.: Evolutionary dynamic multiobjective optimization assisted by a support vector regression predictor. IEEE Trans. Evol. Comput. 24, 305–319 (2020)

    Article  Google Scholar 

  33. Feng, L., Zhou, W., Liu, W., Ong, Y.−S., Tan, K.C.: Solving dynamic multiobjective problem via autoencoding evolutionary search. Cybern IEEE Trans (2020). https://doi.org/10.1109/TCYB.2020.3017017

    Article  Google Scholar 

  34. Iqbal, M., Xue, B., Al−Sahaf, H., Zhang, M.: Cross−domain reuse of extracted knowledge in genetic programming for image classification. IEEE Trans. Evol. Comput. 21, 569–587 (2017)

    Article  Google Scholar 

  35. Feng, L., Zhou, L., Zhong, J., Gupta, A., Ong, Y.S., Tan, K.C., Qin, A.K.: Evolutionary multitasking via explicit autoencoding. IEEE Trans. Cybern. 49, 3457–3470 (2019)

    Article  Google Scholar 

  36. Min, A.T.W., Ong, Y.S., Gupta, A., Goh, C.K.: Multiproblem surrogates: transfer evolutionary multiobjective optimization of computationally expensive problems. IEEE Trans. Evol. Comput. 23, 15–28 (2019)

    Article  Google Scholar 

  37. Zhang, J., Zhou, W., Chen, X., Yao, W., Cao, L.: Multisource selective transfer framework in multiobjective optimization problems. IEEE Trans. Evol. Comput. 24, 424–438 (2020)

    Google Scholar 

  38. Zhou, L., Feng, L., Tan, K.C., Zhong, J., Zhu, Z., Liu, K., Chen, C.: Toward adaptive knowledge transfer in multifactorial evolutionary computation. IEEE Trans. Cybern. 51, 2563–2576 (2021)

    Article  Google Scholar 

  39. Zou, F., Yen, G.G., Tang, L., Wang, C.: A reinforcement learning approach for dynamic multi−objective optimization. Inf. Sci. 546, 815–834 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  40. Ismayilov, G., Topcuoglu, H.R.: Neural network based multi−objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Futur. Gener. Comput. Syst. 102, 307–322 (2020)

    Article  Google Scholar 

  41. Jiang, M., Wang, Z., Qiu, L., Guo, S., Gao, X., Tan, K.C.: A fast dynamic evolutionary multiobjective algorithm via manifold transfer learning. IEEE Trans. Cybern. 51, 3417–3428 (2021)

    Article  Google Scholar 

  42. Coello, C., Pulido, G., Lechuga, M.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8, 256–279 (2004)

    Article  Google Scholar 

  43. Han, H., Wu, X., Liu, Z., Qiao, J.: Design of self−organizing intelligent controller using fuzzy neural network. IEEE Trans. Fuzzy Syst. 26, 3097–3111 (2018)

    Article  Google Scholar 

  44. Jiang, S., Yang, S., Yao, X., Tan, K.C., Kaiser, M.: Benchmark problems for CEC2018 competition on dynamic multiobjective optimisation. CEC2018 Compet. 1–18 (2018). https://www.semanticscholar.org/paper/Benchmark-Problems-for-CEC2018-Competition-on-Jiang-Yang/9bb47fd3d6445d739b1e78aa2d177312d07fac1b#citing-papers

  45. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi−objective optimization test problems. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002. pp. 825–830 (2002)

  46. Han, H., Qiao, J.: A self−organizing fuzzy neural network based on a growing−and−pruning algorithm. IEEE Trans. Fuzzy Syst. 18, 1129–1143 (2010)

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Science Foundation of China under Grants 61890930−5, 61903010, 62021003 and 62125301, Beijing Outstanding Young Scientist Program under Grant BJJWZYJH01201910005020, Beijing Natural Science Foundation under Grant KZ202110005009 and CAAI−Huawei MindSpore Open Fund under Grant CAAIXSJLJJ−2021−017A.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Honggui Han, Yucheng Liu, Hongxu Liu, Hongyan Yang & Junfei Qiao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Honggui Han, Yucheng Liu, Hongxu Liu, Hongyan Yang & Junfei Qiao

  3. Automotive Research Institute, China National Heavy Duty Truck Group Co., LTD, Jinan, 250102, China

    Linlin Zhang

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  1. Honggui Han
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Correspondence to Honggui Han.

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Han, H., Liu, Y., Zhang, L. et al. Knowledge Reconstruction for Dynamic Multi-objective Particle Swarm Optimization Using Fuzzy Neural Network. Int. J. Fuzzy Syst. 25, 1853–1868 (2023). https://doi.org/10.1007/s40815-023-01477-2

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  • DOI: https://doi.org/10.1007/s40815-023-01477-2

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