Abstract
NSGA-II algorithm is one of the most representative multi-objective Evolutionary Algorithms. With the help of elite preserving strategy and fast non-dominated sorting method, NSGA-II can effectively maintain the diversity of population and reduce the computational complexity. It has been widely used to solve different problems. However, traditional crossover and mutation operators in NSGA-II have poor linkage learning ability, so it is not easy for NSGAII to identify and exploit the interaction between variables. To make matters worse, it will inevitably damage randomly good building blocks in solution and make the process of searching the optimal Pareto front extremely difficult. In this paper, we propose an improved NSGA-II based on Markov network that replaces crossover and mutation operators by building Markov networks of promising solutions and sampling the built model to generate new solutions. Markov network can describe, identify and maintain the interaction at the variable level abstractly and accurately, which can identify and protect the good building blocks. At the same time, reduction of the manual parameter setting such as crossover probability will direct the MN-NSGA-II intelligently search for Pareto optimal front. The experimental results also show that the MNNSGA-II is effective and has better global convergence than NSGA-II.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hyoung**, K., Meng-sing, L.: Adaptive directional local search strategy for hybrid evolutionary Multiobjective optimization. Appl. Soft Comput. 19, 290–311 (2014). https://doi.org/10.1016/j.asoc.2014.02.019
Lu, H., Yen, G.G.: Rank-density-based multiobjective genetic algorithm and benchmark test function study. IEEE Trans. Evol. Comput. 7(4), 325–343 (2003)
Mei, Y., Tang, K., Yao, X.: Decomposition-based mimetic algorithm for multiobjective capacitated arc routing problem. IEEE Trans. Evol. Comput. 15(2), 151–165 (2003)
Schutze, O., Lara, A., Coello, C.A.: On the influence of the number of objectives on the hardness of a multiobjective optimization problem. IEEE Trans. Evol. Comput. 15(4), 444–455 (2011). https://doi.org/10.1109/TEVC.2010.2064321
Coello, C.A.: Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput. Intell. Mag. 1(1), 28–36 (2006)
Ngoc-Luong, H., Thi-Thanh-Nguyen, H., Wook-Ahn, C.: Entropy-based efficiency enhancement techniques for evolutionary algorithms. Inf. Sci. 188, 100–120 (2012)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Maoguo, G., Licheng, J., Dongdong, Y., Wen**, M.: Evolutionary multi-objective optimization algorithms. J. Softw. 20(2), 271–289 (2009)
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). https://doi.org/10.1109/4235.996017
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of Evolutionary Methods for Design, Optimization and Control, Barcelona, Spain, vol. 3242, pp. 95–100 (2002). https://doi.org/10.3929/ethz-a-004284029
Zhang, Q., Zhou, A., **, Y.: RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans. Evol. Comput. 12(1), 41–63 (2008)
Qu, B.Y., Suganthan, P.N.: Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection. Inf. Sci. 180(17), 317–318 (2010). https://doi.org/10.1016/j.ins.2010.05.013
Srinivas, N., Deb, K.: Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol. Comput. 2(3), 221–248 (1995)
Verma, S., Pant, M., Snasel, V.: A comprehensive review on NSGA-II for multiobjective combinatorial optimization problems. IEEE Access 9, 57757–57791 (2021). https://doi.org/10.1109/ACCESS.2021.3070634
Ji, B., Sun, H., Yuan, X., Yuan, Y., Wang, X.: Coordinated optimized scheduling of locks and transshipment in inland waterway transportation using binary NSGA-II. Int. Trans. Oper. Res. 27(3), 1501–1525 (2020). https://doi.org/10.1111/itor.12720
Yılmaz, Ö.F.: Operational strategies for seru production system a bi-objective optimisation model and solution methods. Int. J. Prod. Res, 58(11), 3195–3219 (2020). https://doi.org/10.1080/00207543.2019.1669841
Hauschild, M., Pelikan, M.: An introduction and survey of estimation of distribution algorithms. Swarm Evol. Comput. 1(1), 111–128 (2011). https://doi.org/10.1016/j.swevo.2011.08.003
Larrañaga, P., Karshenas, H., Bielza, C., Santana, R.: A review on probabilistic graphical models in evolutionary computation. J. Heuristics 18(5), 795–819 (2012). https://doi.org/10.1007/s10732-012-9208-4
Shakya, S., Santana, R., Lozano, J.A.: A Markovianity based optimisation algorithm. Genet. Program Evolvable Mach. 13, 159–195 (2012). https://doi.org/10.1007/s10710-011-9149-y
Acknowledgements
This work is partially supported by Guangdong Province University Characteristic Innovation Project (2020KTSCX231) and National Natural Science Foundation of China (12171162).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kong, Y., Yao, J., Wang, J., Huang, P., Qiu, Z. (2024). An Improved NSGA-II Algorithm with Markov Networks. In: Li, K., Liu, Y. (eds) Intelligence Computation and Applications. ISICA 2023. Communications in Computer and Information Science, vol 2146. Springer, Singapore. https://doi.org/10.1007/978-981-97-4393-3_1
Download citation
DOI: https://doi.org/10.1007/978-981-97-4393-3_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-4392-6
Online ISBN: 978-981-97-4393-3
eBook Packages: Computer ScienceComputer Science (R0)