Abstract
The study of many-objective evolutionary algorithm (MaOEA) has become particularly important, especially with the increasing complex engineering optimization problems. Considering that the convergence and diversity of the population are two important indicators to measure the performance of the algorithm, a many-objective evolutionary algorithm with threshold elite selection strategy (MaOEA-TES) are proposed in this paper. The algorithm adopts the balanceable fitness estimation strategy and the reference-point based non-dominated sorting strategy to balance the convergence and diversity of the solution. An adaptive penalty distance boundary intersection strategy is designed to dynamically adjust the impact of convergence and diversity on the algorithm. In addition, a dynamic threshold selection strategy is proposed to ensure that the algorithm emphasizes diversity at an early stage, emphasizes convergence at a later stage, and ensures that the result is closer to the real non-dominant front. The DTLZ test suite is used to evaluate the performance of MaOEA-TES. The experimental results show that the MaOEA-TES has the best performance comparing with three other state-of-the-art algorithms on many-objective optimization.
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
References
Coello, C.C., Cortés, N.C.: An approach to solve multiobjective optimization problems based on an artificial immune system. In: First International Conference on Artificial Immune Systems (ICARIS 2002), pp. 212–221 (2002)
Cui, Z., Li, F., Zhang, W.: Bat algorithm with principal component analysis. Int. J. Mach. Learn. Cybernet (2018). https://doi.org/10.1007/s13042-018-0888-4
Cai, X., (ed.): Bat algorithm with triangle-flipping strategy for numerical optimization. Int. J. Mach. Learn. Cybern. 9(2), 199–215 (2018)
Li, M., Yang, S., Liu, X.: Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Trans. Evol. Comput. 18(3), 348–365 (2014)
Wickramasinghe, U.K., Carrese, R., Li, X.: Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010). https://doi.org/10.1109/CEC.2010.5586221
Fu, G., Kapelan, Z., Kasprzyk, J.R., Reed, P.: Optimal design of water distribution systems using many-objective visual analytics. J. Water Resour. Plann. Manag. 139, 624–633 (2013)
Lygoe, R., Cary, M., Fleming, P.: A real-world application of a many-objective optimisation complexity reduction process. In: Purshouse, R., Fleming, P., Fonseca, C., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 641–655. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37140-048
Fleming, P.J., Purshouse, R.C., Lygoe, R.J.: Many-objective optimization: an engineering design perspective. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 14–32. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31880-4_2
Mkaouer, M.W., (ed.): High dimensional search-based software engineering: finding tradeoffs among 15 objectives for automating software refactoring using NSGA-III. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, Vancouver, BC, Canada, 12–16 July, pp. 1263–1270 (2014)
Cui, Z.H., et al.: A pigeon inspired optimization algorithm for many-objective optimization problems. Sci. China Inf. Sci. 62(7), 070212 (2019)
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)
Bi, X., Wang, C.: A niche-elimination operation based NSGA-III algorithm for many-objective optimization. Appl. Intell. 48(1), 118–141 (2017). https://doi.org/10.1007/s10489-017-0958-4
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)
Zitzler, E., Künzli, S.: Indicator-based selection in multi-objective 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
Lin, Q.Z., et al.: Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems. IEEE Trans. Evol. Comput. 22(1), 32–46 (2018)
Deb, K., (ed.): Scalable test problems for evolutionary multi-objective optimization. In: Evolutionary Multi-objective Optimization, pp. 105–145 (2005)
Zhang, Q., (ed.): Multi-objective optimization test instances for the CEC 2009 special session and competition, p. 264. Technical report, University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms (2008)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No.61806138, Natural Science Foundation of Shanxi Province under Grant No. 201801D121127, Taiyuan University of Science and Technology Scientific Research Initial Funding under Grant No. 20182002. Postgraduate education Innovation project of Shanxi Province under Grant No. 2019SY493.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Geng, S., Wu, D., Wang, P., Cai, X. (2020). A Many-Objective Algorithm with Threshold Elite Selection Strategy. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-5577-0_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5576-3
Online ISBN: 978-981-15-5577-0
eBook Packages: Computer ScienceComputer Science (R0)