Abstract
In Non Dominated Sorting Genetic Algorithm-III (NSGA-III), the diversity of solutions is guided by a set of uniformly distributed reference points in the objective space. However, uniformly distributed reference points may not be efficient for problems with disconnected and non-uniform Pareto-fronts. These kinds of problems may have some reference points that are never associated with any of the Pareto-optimal solutions and will become useless reference points during evaluation. The existence of these useless reference points in NSGA-III significantly affects its performance. To address this issue, a new reference points adaptation mechanism is proposed that generates reference points according to the distribution of the candidate solutions. The use of this proposed adaptation method improves the performance of evolutionary search and promotes population diversity for better exploration. The proposed approach is evaluated on a number of unconstrained benchmark problems and is compared with NSGA-III and other reference point adaptation approaches. Experiment results on several benchmark problems clearly show a prominent improvement in the performance by using the proposed reference point adaptation mechanism in NSGA-III.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chand, S., Wagner, M.: Evolutionary many-objective optimization: a quick-start guide. Surv. Oper. Res. Manag. Sci. 20(2), 35–42 (2015)
Cheng, R., et al.: A benchmark test suite for evolutionary many-objective optimization. Complex Intell. Syst. 3(1), 67–81 (2017)
Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)
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)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Deb, K., Thiele, L., Zitzler, E.: Scalable multi-objectove optimization test problems. In: IEEE Congress on Evolutionary Computation, pp. 825–830. IEEE (2002)
López Jaimes, A., Coello Coello, C.A.: Many-objective problems: challenges and methods. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 1033–1046. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-43505-2_51
Jain, H., Deb, K.: An improved adaptive approach for elitist nondominated sorting genetic algorithm for many-objective optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 307–321. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37140-0_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)
Jiang, S., Yang, S.: A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization. IEEE Trans. Evol. Comput. 21(3), 329–346 (2017)
Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 48(1), 13 (2015)
Liu, Y., Gong, D., Sun, X., Zhang, Y.: A reference points-based evolutionary algorithm for many-objective optimization. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 1053–1056. ACM (2014)
Masood, A., Chen, G., Mei, Y., Zhang, M.: Reference point adaption method for genetic programming hyper-heuristic in many-objective job shop scheduling. In: Liefooghe, A., López-Ibáñez, M. (eds.) EvoCOP 2018. LNCS, vol. 10782, pp. 116–131. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77449-7_8
Masood, A., Mei, Y., Chen, G., Zhang, M.: Many-objective genetic programming for job-shop scheduling. In: IEEE WCCI 2016 Conference Proceedings. IEEE (2016)
Masood, A., Mei, Y., Chen, G., Zhang, M.: A PSO-based reference point adaption method for genetic programming hyper-heuristic in many-objective job shop scheduling. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS (LNAI), vol. 10142, pp. 326–338. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51691-2_28
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. Technical report, University of Essex, Colchester, UK and Nanyang Technological University, Singapore, Special Session on Performance Assessment of Multi-objective Optimization Algorithms, pp. 1–30 (2008)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Masood, A., Chen, G., Mei, Y., Zhang, M. (2018). Adaptive Reference Point Generation for Many-Objective Optimization Using NSGA-III. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-03991-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03990-5
Online ISBN: 978-3-030-03991-2
eBook Packages: Computer ScienceComputer Science (R0)