Abstract
Non-dominated sorting genetic algorithm II (NSGA-II) obtains a great success for solving multi-objective optimization problems (MOPs). It uses a tournament selection operator (TSO) to select the suitable individuals for the next generation. However, TSO selects individuals based on the non-dominated rank and the crowding distance of each individual, which exhausts a lot of computational burden. In order to relieve the heavy computational burden, this paper proposes an improved selection operator (ISO) that is based on two selection schemes, i.e., a rank-based selection (S-Rank) and a random-based selection (S-Rand). S-Rank is a scheme that selects individuals based on its non-dominated ranks, in which if the individuals have the different non-dominated ranks, the individuals with lower (better) ranks will be selected for the next generation. On the contrary, if the individuals have the same rank, we first select an objective randomly from all objectives, and then select the individual with the better fitness on this objective to enter the next generation. This is the S-Rand scheme that can increase the diversity of individuals (solutions) due to the random selection of objective. The proposed ISO only calculates the crowding distance of the last (selected) rank individual, and avoids the calculation of the crowding distance of all individuals. The performance of ISO is tested on two different benchmark sets: the ZDT test set and the UF test set. Experimental results show that ISO effectively reduces the computational burden and enhance the selection diversity by the aid of S-Rank and S-Rand.
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
Chen, Z.G., et al.: Multiobjective cloud workflow scheduling: a multiple populations ant colony system approach. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/tcyb.2018.2832640
Yu, X., et al.: Set-based discrete particle swarm optimization based on decomposition for permutation-based multiobjective combinatorial optimization problems. IEEE Trans. Crbern. 48(7), 2139–2153 (2018)
Chen, N., et al.: An evolutionary algorithm with double-level archives multiobjective optimization. IEEE Trans. Cybern. 45(9), 1851–1863 (2015)
Lin, Q.Z., et al.: A hybrid evolutionary immune algorithm for multiobjective optimization problems. IEEE Trans. Evol. Comput. 20(5), 711–729 (2016)
Parsopoulos, K.E., Vrahatis, M.N.: Particles swarm optimization method in multiobjective problems. In: Proceedings ACM Symposium Applications Computation, pp. 603–607 (2002)
Zhan, Z.H., Li, J.J., Cao, J.N., Zhang, J., Chung, H.S., Shi, Y.H.: Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems. IEEE Trans. Cybern. 43(2), 445–463 (2013)
Liu, X.F., Zhan, Z.H., Gao, Y., Zhang, J., Kwong, S., Zhang, J.: Coevolutionary particle swarm optimization with bottleneck objective learning strategy for many-objective optimization. IEEE Trans. Evol. Comput. (2018). https://doi.org/10.1109/tevc.2018.2875430
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
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)
Li, Y.Z., Wu, Q.H., Li, M.S., Zhan, J.P.: Mean-variance model for power system economic dispatch with wind power integrated. Energy 72(1), 510–520 (2014)
Li, Y.Z., Li, M.S., Wen, B.J., Wu, Q.H.: Power system dispatch with wind power integrated using mean-variance model and group search optimizer. In: 2014 IEEE PES General Conference & Exposition, National Harbor, MD, pp. 1–5 (2014)
Ismail, H.A., Packianather, M.S., Grosvenor, R.I.: Multi-objective invasive weed optimization of the LQR controller. Int. J. Autom. Comput. 3, 321–339 (2017)
Yao, G.S., Ding, Y.S., Jin, Y.C., Hao, K.G.: Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system. Soft. Comput. 21(15), 4309–4322 (2017)
Murugan, P., Kannan, S., Baskar, S.: Application of NSGA-II algorithm to single-objective transmission constrained generation expansion planning. IEEE Trans. Power Syst. 24(4), 1790–1797 (2009)
Singh, U., Singh, S.N.: Optimal feature selection via NSGA-II for power quality disturbances classification. IEEE Trans. Ind. Inform. 14(7), 2994–3002 (2018)
Jayaprakasam, S., Abdul Rahim, S.K., Leow, C.Y., Ting, T.O., Eteng, A.A.: Multiobjective beampattern optimization in collaborative beamforming via NSGA-II with selective distance. IEEE Trans. Antennas Propag. 65(5), 2348–2357 (2017)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Zhang, Q.F., Zhou, A., Zhao, S.Z., Suganthan, P.N., Liu, W.D., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. In: Proceedings of IEEE Congress Evolutionary Computation, pp. 1–30 (2009)
Acknowledgments
This work was partially supported by the Outstanding Youth Science Foundation with No. 61822602, the National Natural Science Foundations of China (NSFC) with No. 61772207 and 61873097, the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars with No. 2014A030306038, the Project for Pearl River New Star in Science and Technology with No. 201506010047, the GDUPS (2016), the Science and Technology Planning Project of Guangdong Province, China, with No 2014B050504005, and the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, H. et al. (2019). An Improved Selection Operator for Multi-objective Optimization. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-22796-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22795-1
Online ISBN: 978-3-030-22796-8
eBook Packages: Computer ScienceComputer Science (R0)