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Combining Dynamic Constrained Many-Objective Optimization with DE to Solve Constrained Optimization Problems

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Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

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Abstract

This paper proposes a dynamic constrained many-objective optimization method for solving constrained optimization problems. We first convert a constrained optimization problem (COP) into an equivalent dynamic constrained many-objective optimization problem (DCMOP), then present many-objective optimization evolutionary algorithm with dynamic constraint handling mechanism, called MaDC, to solve the DCMOP, thus the COP is addressed. MaDC uses DE as the search engine, and reference-point-based nondominated sorting approach to select individuals to construct next population. The effectiveness of MaDC has been verified by comparing with peer algorithms.

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References

  1. Mezura-Montes, E., Coello, C.A.C.: Constraint handling in nature-inspired numerical optimization: Past, present and future. Swarm Evol. Comput. 1(4), 173–194 (2011)

    Article  Google Scholar 

  2. Kramer, O.: A review of constraint-handling techniques for evolution strategies. In: Applied Computational Intelligence and Soft Computing, vol. 2010 (2010)

    Google Scholar 

  3. Sarker, R.A., Elsayed, S.M., Ray, T.: Differential evolution with dynamic parameters selection for optimization problem. IEEE Trans. Evol. Comput. 18(5), 689–707 (2014)

    Article  Google Scholar 

  4. Coello, C.A.C.: Constraint-handling using an evolutionary multi-objective optimization technique. Civil Eng. Environ. Syst. 17, 319–346 (2000)

    Article  Google Scholar 

  5. Coello, C.A.C., Mezura-Montes, E.: Constraint-handling in genetic algorithms through the use of dominance-based tournament election. Adv. Eng. Inform. 16(3), 193–203 (2002)

    Article  Google Scholar 

  6. Venkatraman, S., Yen, G.G.: A generic framework for constrained optimization using genetic algorithms’. IEEE Trans. Evol. Comput. 9(4), 424–435 (2005)

    Article  Google Scholar 

  7. Hsieh, M., Chiang, T., Fu, L.: A hybrid constraint handling mechanism with differential evolution for constrained multiobjective optimization. In: IEEE Congress on Evolutionary Computation, pp. 1785–1792 (2011)

    Google Scholar 

  8. Wang, Y., Cai, Z., Guo, G., Zhou, Y.: Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Trans. Syst. Man Cybern. 37(3), 560–575 (2007)

    Article  Google Scholar 

  9. Deb, K., Datta, R.: A fast and accurate solution of constrained optimization problems using a hybrid bi-objective and penalty function approach. In: IEEE Congress on Evolutionary Computation, pp. 165–172 (2010)

    Google Scholar 

  10. Zeng, S., Chen, S., Zhao, J., Zhou, A., Li, Z., Jing, H.: Dynamic constrained multi-objective model for solving constrained optimization problem. In: IEEE Congress on Evolutionary Computation, pp. 2041–2046 (2011)

    Google Scholar 

  11. Li, X., Zeng, S., Qin, S., Liu, K.: Constrained optimization problem solved by dynamic constrained NSGA-III multiobjective optimizational techniques. In: IEEE Congress on Evolutionary Computation, pp. 2923–2928 (2015)

    Google Scholar 

  12. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)

    Article  Google Scholar 

  13. Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello, C.A.C., Deb, K.: Problem definitions and evaluation criteria for the CEC2006 special session on constrained real-parameter optimization (2006). http://www.ntu.edu.sg/home/epnsugan/

  14. Elsayed, S.M., Sarker, R.A., Essam, D.L.: Multi-operator based evolutionary algorithms for solving constrained optimization problems. Comput. Oper. Res. 38(12), 1877–1896 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  15. Mallipeddi, R., Suganthan, P.N.: Ensemble of constraint handling techniques. IEEE Trans. Evol. Comput. 14(4), 561–579 (2010)

    Article  Google Scholar 

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Acknowledgment

This work was supported by the National Natural Science Foundation of China and other foundations(No.s: 61271140, 61203306, 2012001202, 61305086).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, Hubei, People’s Republic of China

    Xi Li, Sanyou Zeng, Liting Zhang & Guilin Zhang

  2. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang, 050031, Hebei, People’s Republic of China

    Xi Li

Authors
  1. Xi Li
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  2. Sanyou Zeng
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  3. Liting Zhang
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  4. Guilin Zhang
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Corresponding author

Correspondence to Sanyou Zeng .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Li, X., Zeng, S., Zhang, L., Zhang, G. (2016). Combining Dynamic Constrained Many-Objective Optimization with DE to Solve Constrained Optimization Problems. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_7

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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