Abstract
Selecting the appropriate meta-features to represent the optimization problems was studied previously. However, the research on the extraction of meta-features for multi-objective problems is lacking. In this paper, a set of meta-features including a unique meta-feature based on Pareto front shape and the combination of meta-features are proposed for the multi-objective optimization problems (MOPs). 25 multi-objective benchmark functions and K-NN algorithm are adopted to realize the algorithm recommendation for MOPs. Experimental results show that the meta-features based on Pareto front can properly represent multi-objective problems and obtain better recommendation performance. The algorithm recommendation accuracy is improved once the combination of meta-features is considered.
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
Li, K., Wang, R., Zhang, T.: Evolutionary many-objective optimization: a comparative study of the state-of-the-art. IEEE Access 6, 26194–26214 (2018)
Ma, X., Yang, J., Wu, N.: A comparative study on decomposition-based multi-objective evolutionary algorithms for many-objective optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2477–2483 (2016)
Wang, R., Zhou, Z., Ishibuchi, H.: Localized weighted sum method for many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 3–18 (2018)
Tanabe, R., Ishibuchi, H.: A review of evolutionary multimodal multiobjective optimization. IEEE Trans. Evol. Comput. 24(1), 193–200 (2020)
Huband, S., Hingston, P., Barone, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: the strength pareto approach. TIK-report, vol. 43 (1998)
Zhang, Q., Li, H.: MOEA/D: a multiobjective 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 (2013)
Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-report, vol. 103 (2001)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_84
Chu, X., Cai, F., Cui, C.: Adaptive recommendation model using meta-learning for population-based algorithms. Inf. Sci. 476, 192–210 (2019)
Kanda, J., De Carvalho, A., Hruschka, E.: Meta-learning to select the best meta-heuristic for the traveling salesman problem: a comparison of meta-features. Neurocomputing 205, 393–406 (2016)
Brazdil, P., Carrier, C.G., Soares, C.: Metalearning: Applications to Data Mining. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-73263-1
Peng, Y., Flach, P.A., Soares, C., Brazdil, P.: Improved dataset characterisation for meta-learning. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 141–152. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36182-0_14
Vilalta, R., Giraud-Carrier, C.G., Brazdil, P.: Using meta-learning to support data mining. Int. J. Comput. Sci. Appl. 1(1), 31–45 (2004)
Alcobaca, E., Siqueira, F., Rivolli, A.: MFE: towards reproducible meta-feature extraction. J. Mach. Learn. Res. 21, 1–5 (2020)
Rivolli, A., Garcia, L. P., Soares, C.: Towards reproducible empirical research in meta-learning, pp. 32–52 (2018)
Reif, M., Shafait, F., Goldstein, M., Breuel, T., Dengel, A.: Automatic classifier selection for non-experts. Pattern Anal. Appl. 17(1), 83–96 (2012). https://doi.org/10.1007/s10044-012-0280-z
Balte, A., Pise, N., Kulkarni, P.: Meta-learning with landmarking: a survey. Int. J. Comput. Appl. 105, 8 (2014)
Pinto, F., Soares, C., Mendes-Moreira, J.: Towards automatic generation of metafeatures. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9651, pp. 215–226. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31753-3_18
Bossek, J.: smoof: single-and multi-objective optimization test functions. R J. 9(1), 103 (2017)
Ishibuchi, H., Setoguchi, Y., Masuda, H.: Performance of decomposition-based many-objective algorithms strongly depends on pareto front shapes. IEEE Trans. Evol. Comput. 21(2), 169–190 (2017)
Cui, C., Hu, M.Q., Weir, J.D.: A recommendation system for meta-modeling: a meta-learning based approach. Expert Syst. Appl. 46, 33–44 (2016)
Tian, Y., Cheng, R., Zhang, X.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)
Neave, H., Worthington, P.: Distribution-free tests. Contemp. Sociol. 19(3), 137–153 (1990)
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (Grant No. 71971142 and 71701079).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chu, X. et al. (2021). Meta-feature Extraction for Multi-objective Optimization Problems. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_31
Download citation
DOI: https://doi.org/10.1007/978-981-16-5188-5_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5187-8
Online ISBN: 978-981-16-5188-5
eBook Packages: Computer ScienceComputer Science (R0)