Advertisement

A Multi-objective Optimization Algorithm Based on Preference Three-Way Decomposition

  • Zhao Fu
  • Hong Yu
  • Hongliang Zhang
  • Xiaofang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

Most of refining processes were optimized using single objective approach, but practically such complex processes must be optimized with several objectives. Inspired by the theory of three-way decisions, a multi-objective optimization algorithm based on preference three-way decomposition is proposed in this paper. First, according to the preferences of the DM, the analytic hierarchy process (AHP) is used to sort objectives. Then, based on the idea of three-way decisions, these objectives are divided into three sub-parts as the primary objective set, the secondary objective set and the general objective set. Besides, a multi-group parallel optimization algorithm is presented to solve each sub-optimization problem. Finally, based on Non-dominated set of the three sub-problems, a set of external preservation sets are formed so as to get the optimal set that the DM is interested in. Experimental results show that the proposed method can reduce the workload of the DM and obtain more accurately converge to the optimal frontiers of the optimization problems.

Keywords

Multi-objective optimization Decision making Preference information Three-way decisions Decomposition 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61751312, 61533020 and 61379114.

References

  1. 1.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London (2005).  https://doi.org/10.1007/1-84628-137-7_6CrossRefMATHGoogle Scholar
  2. 2.
    Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  3. 3.
    Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Conference on Genetic and Evolutionary Computation, pp. 635–642. ACM (2006)Google Scholar
  4. 4.
    Gong, D.W., Liu, Y.P., Sun, X.Y., et al.: Parallel many-objective evolutionary optimization using objectives decomposition. Acta Autom. Sin. 41(8), 1438–1451 (2015)MATHGoogle Scholar
  5. 5.
    Kaddani, S., Vanderpooten, D., Vanpeperstraete, J.M., et al.: Weighted sum model with partial preference information: PineGreen application to multi-objective optimization. Eur. J. Oper. Res. 260(2), 665–679 (2017)CrossRefGoogle Scholar
  6. 6.
    Narzisi, G.: Multi-objective optimization: a quick introduction. New York University lectures (2008)Google Scholar
  7. 7.
    Purshouse, R.C., Fleming, P.J.: Evolutionary many-objective optimisation: an exploratory analysis. Evol. Comput. 3, 2066–2073 (2003). CECGoogle Scholar
  8. 8.
    Shen, X., Yu, G., Chen, Q., et al.: A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic. Comput. Optim. Appl. 46(1), 159–188 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Yao, Y.Y.: The superiority of three-way decisions in probabilistic rough set models. Inf. Sci. 181(6), 1080–1096 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zeman, J.: Ontological and gnoseological aspects of contradiction and their importance in analysis of the development of scientific knowledge. In: Handbook of Professional Ethics for Psychologists (1984)Google Scholar
  11. 11.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhao Fu
    • 1
  • Hong Yu
    • 1
  • Hongliang Zhang
    • 2
  • Xiaofang Chen
    • 3
  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China
  2. 2.School of Metallurgy and EnvironmentCentral South UniversityChangshaPeople’s Republic of China
  3. 3.School of Information Science and EngineeringCentral South UniversityChangshaPeople’s Republic of China

Personalised recommendations