Abstract
The number of objectives in real-world problems has increased in recent years and better algorithms are needed to deal efficiently with it. One possible improvement to such algorithms is the use of adaptive operator selection mechanisms in many-objective optimization algorithms. In this work, two adaptive operator selection mechanisms, Probability Matching (PM) and Adaptive Pursuit (AP), are incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving a many-objective problem. Our proposed approaches, NSGA-III\(_{\text {AP}}\) and NSGA-III\(_{\text {PM}}\), are tested on benchmark instances from the DTLZ and WFG test suits and on instances of the Protein Structure Prediction Problem. Statistical tests are performed to infer the significance of the results. The preliminary results of the proposed approaches are encouraging.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The reference Pareto Front R is a set of N uniformly distributed points on each problem. It can be calculated using a generator code available at https://github.com/JerryI00/SamplingPF.
References
Brasil, C.R.S., Delbem, A.C.B., da Silva, F.L.B.: Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction. J. Comput. Chem. 34(20), 1719–1734 (2013)
Coello, C.A.C., Cortés, N.C.: Solving multiobjective optimization problems using an artificial immune system. Genet. Program. Evolvable Mach. 6(2), 163–190 (2005). http://dx.doi.org/10.1007/s10710-005-6164-x
Conover, W.: Practical Nonparametric Statistics, 3rd edn. Wiley, New York (1999)
Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9(2), 115–148 (1995)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_83
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, pp. 105–145. Springer, London (2005)
Durillo, J.J., Nebro, A.J.: Jmetal: a java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011)
Gonçalves, R., Almeida, C., Pavelski, L., Venske, S., Kuk, J., Pozo, A.: Adaptive Operator Selection in NSGA-III. In: To appear in Proceedings of Brazilian Conference on Intelligent Systems, BRACIS 2016 (2016)
Gong, W., Fialho, Á., Cai, Z., Li, H.: Adaptive strategy selection in differential evolution for numerical optimization: an empirical study. Inf. Sci. 181(24), 5364–5386 (2011)
Handl, J., Lovell, S.C., Knowles, J.: Investigations into the effect of multiobjectivization in protein structure prediction. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 702–711. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87700-4_70
Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: Proceedings of 2008 IEEE Congress on Evolutionary Computation, pp. 2424–2431 (2008)
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)
Li, K., Deb, K., Zhang, Q., Kwong, S.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694–716 (2015)
Li, K., Fialho, Á., Kwong, S.: Multi-objective differential evolution with adaptive control of parameters and operators. In: International Conference on Learning and Intelligent Optimization, pp. 473–487 (2011)
Li, K., Fialho, A., Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 18(1), 114–130 (2014)
Lin, Q., Liu, Z., Yan, Q., Du, Z., Coello, C.A.C., Liang, Z., Wang, W., Chen, J.: Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inf. Sci. 339, 332–352 (2016)
MacKerell, A.D., Banavali, N., Foloppe, N.: Development and current status of the charmm force field for nucleic acids. Biopolymers 56(4), 257–265 (2000)
Mkaouer, W., Kessentini, M., Shaout, A., Koligheu, P., Bechikh, S., Deb, K., Ouni, A.: Many-objective software remodularization using NSGA-III. ACM Trans. Softw. Eng. Methodo. 24(3), 17:1–17:45 (2015)
Ponder, J.W.: Tinker: Software tools for molecular design. Washington University School of Medicine, Saint Louis, MO 3 (2004)
Seada, H., Deb, K.: U-NSGA-III: a unified evolutionary optimization procedure for single, multiple, and many objectives: proof-of-principle results. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 34–49. Springer, Cham (2015). doi:10.1007/978-3-319-15892-1_3
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Conference on Genetic and Evolutionary Computation, pp. 1539–1546 (2005)
Tramontano, A.: Protein Structure Prediction: Concepts and Applications. Wiley, New Year (2006)
Venske, S.M., Gonçalves, R.A., Delgado, M.R.: ADEMO/D: multiobjective optimization by an adaptive differential evolution algorithm. Neurocomputing 127, 65–77 (2014)
Yuan, Y., Xu, H., Wang, B.: An improved NSGA-III procedure for evolutionary many-objective optimization. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 661–668. ACM, New York (2014)
Yuan, Y., Xu, H., Wang, B.: An experimental investigation of variation operators in reference-point based many-objective optimization. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 775–782. ACM, New York (2015)
Zaki, M., Bystroff, C. (eds.): Protein Structure Prediction. Methods in Molecular Biology, vol. 413, 2nd edn. Humana Press/Springer, Heidelberg (2008)
Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans. Evol. Comput. 16(3), 442–446 (2012)
Acknowledgments
The authors acknowledge CNPq Grants 456179/2014-3, 483974/2013-7, and 311605/2011-7 for the partial financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gonçalves, R.A., Pavelski, L.M., de Almeida, C.P., Kuk, J.N., Venske, S.M., Delgado, M.R. (2017). Adaptive Operator Selection for Many-Objective Optimization with NSGA-III. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-54157-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54156-3
Online ISBN: 978-3-319-54157-0
eBook Packages: Computer ScienceComputer Science (R0)