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Adaptive Constraint Multi-objective Differential Evolution Algorithm Based on SARSA Method

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1565))

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Abstract

The performance of constrained multi-objective differential evolution algorithm is mainly determined by constraint handling techniques (CHTs) and its generation strategies. Moreover, CHTs have different search capabilities and each generation strategy in a differential evolution is applicable to particular type of constrained multi-objective optimization problems (CMOPs). To automatically select appropriate CHT and generation strategy, an adaptive constrained multi-objective differential evolution algorithm based on state–action–reward–state–action (SARSA) approach (ACMODE) is introduced. In the ACMODE, the SARSA is used to select suitable CHT and generation strategy to solve particular types of CMOPs. The performance of the proposed algorithm is compared with other four famous constrained multi-objective evolutionary algorithms (CMOEAs) on 15 CMOPs. Experimental results show that the overall performance of the ACMODE is the best among all competitors.

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References

  1. Maminov, A., Posypkin, M.: Constrained multi-objective robot’s design optimization. In: 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1992-1995 (2020)

    Google Scholar 

  2. liu, J., Yang, Y., Tan, S., Wang, H.: Application of constrained multi-objective evolutionary algorithm in a compressed-air station scheduling problem. In: 2019 Chinese Control Conference (CCC), pp. 2023–2028 (2019)

    Google Scholar 

  3. Li,B., Wang, J., Xia, N.: Dynamic optimal scheduling of microgrid based on ε constraint multi-objective biogeography-based optimization algorithm. In: 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), pp. 389–393 (2020)

    Google Scholar 

  4. Wang, J., Li, Y., Zhang, Q., Zhang, Z., Gao, S.: Cooperative multiobjective evolutionary algorithm with propulsive population for constrained multiobjective optimization. IEEE Trans. Syst. Man Cybernet. Syst. 1–16 (2021)

    Google Scholar 

  5. Datta, R., Deb, K., Segev, A.: A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 317–324 (2017)

    Google Scholar 

  6. Yuan, J., Liu, H.L., Ong, Y.S., He, Z.: Indicator-based evolutionary algorithm for solving constrained multi-objective optimization problems. IEEE Trans. Evol. Comput. 1 (2021)

    Google Scholar 

  7. Cui, C.X., Fan, Q.Q.: Constrained multi-objective differential evolutionary algorithm with adaptive constraint handling technique. World Sci. Res. J. 7, 322–339 (2021)

    Google Scholar 

  8. Richard, S.S., Andrew, G.B.: Temporal-difference learning. In: Reinforcement Learning: An Introduction, pp. 133–160, MIT Press (1998)

    Google Scholar 

  9. Lin, Y., Du, W., Du, W.: Multi-objective differential evolution with dynamic hybrid constraint handling mechanism. Soft. Comput. 23(12), 4341–4355 (2018). https://doi.org/10.1007/s00500-018-3087-z

    Article  Google Scholar 

  10. Tian, Y., Zhang, T., Xiao, J., Zhang, X., Jin, Y.: A Coevolutionary framework for constrained multiobjective optimization problems. IEEE Trans. Evol. Comput. 25(1), 102–116 (2021)

    Article  Google Scholar 

  11. Fan, Z., et al.: Push and pull search for solving constrained multi-objective optimization problems. Swarm Evol. Comput. 44, 665–679 (2019)

    Article  Google Scholar 

  12. Liu, Z.Z., Wang, Y.: Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces. IEEE Trans. Evol. Comput. 23(5), 870–884 (2019)

    Article  Google Scholar 

  13. Liu, Z.Z., Wang, Y., Wang, B.C.: Indicator-based constrained multiobjective evolutionary algorithms. IEEE Trans. Syst. Man Cybernet. Syst. 51(9), 5414–5426 (2021)

    Article  Google Scholar 

  14. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  15. Xu, B., Duan, W., Zhang, H., Li, Z.: Differential evolution with infeasible-guiding mutation operators for constrained multi-objective optimization. Appl. Intell. 50(12), 4459–4481 (2020). https://doi.org/10.1007/s10489-020-01733-0

    Article  Google Scholar 

  16. Liu, B.J., Bi, X.J.: Adaptive ε-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution. IEEE Access 9, 17596–17609 (2021)

    Article  Google Scholar 

  17. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  18. Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 174–188 (2003)

    Article  Google Scholar 

  19. 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 

  20. Woldesenbet, Y.G., Yen, G.G., Tessema, B.G.: Constraint handling in multiobjective evolutionary optimization. IEEE Trans. Evol. Comput. 13(3), 514–525 (2009)

    Article  Google Scholar 

  21. 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 

  22. Wang, Y., Cai, Z., Zhou, Y., Zeng, W.: An adaptive tradeoff model for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 12(1), 80–92 (2008)

    Article  Google Scholar 

  23. Shahrabi, J., Adibi, M.A., Mahootchi, M.: A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput. Ind. Eng. 110, 75–82 (2017)

    Article  Google Scholar 

  24. Jan, M.A., Khanum, R.A.: A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D. Appl. Soft Comput. 13(1), 128–148 (2013)

    Article  Google Scholar 

  25. Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)

    Article  Google Scholar 

  26. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Deb, K., Pratap, A., Meyarivan, T.: Constrained test problems for multi-objective evolutionary optimization. In: Presented at the first international conference on evolutionary multi-criterion optimization (EMO), Zurich, Switzerland (2000)

    Google Scholar 

  29. Srinivas, N., Deb, K.: Multiobjective function optimization using nondominated sorting genetic algorithms. IEEE Trans. Evol. Comput. 2(3), 1301–1308 (1994)

    Google Scholar 

  30. Osyczka, A., Kundu, S.: A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Struct. Optim. 10(2), 94–99 (1995)

    Article  Google Scholar 

  31. Tanaka, M., Watanabe, H., Furukawa, Y., Tanino, T.: GA-based decision support system for multicriteria optimization. In: 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, vol. 2, pp. 1556–1561 (1995)

    Google Scholar 

  32. Binh, T.T., Korn, U.: MOBES: a multiobjective evolution strategy for constrained optimization problems. In: Presented at the third international conference on genetic algorithms, Mendel (1997)

    Google Scholar 

  33. Ray, T., Liew, K.M.: A swarm metaphor for multiobjective design optimization. Eng. Optim. 34(2), 141–153 (2002)

    Article  Google Scholar 

  34. Coello Coello, C.A., Pulido, G.T.: Multiobjective structural optimization using a microgenetic algorithm. Struct. Multidiscip. Optim. 30(5), 388–403 (2005)

    Google Scholar 

  35. Justesen, P.D.: Multi-objective optimization using evolutionary algorithms. University of Aarhus, Department of Computer Science (2009)

    Google Scholar 

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Authors and Affiliations

  1. Logistics Research Center, Shanghai Maritime University, Shanghai, 201306, China

    Qingqing Liu, Caixia Cui & Qinqin Fan

Authors
  1. Qingqing Liu
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  2. Caixia Cui
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  3. Qinqin Fan
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Corresponding author

Correspondence to Qinqin Fan .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

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Liu, Q., Cui, C., Fan, Q. (2022). Adaptive Constraint Multi-objective Differential Evolution Algorithm Based on SARSA Method. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_17

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  • DOI: https://doi.org/10.1007/978-981-19-1256-6_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1255-9

  • Online ISBN: 978-981-19-1256-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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