Abstract
MOEA/D is a novel multiobjective evolutionary algorithm based on decomposition approach, which has attracted much attention in recent years. However, when tackling the problems with irregular (e.g., disconnected or degenerated) Pareto fronts (PFs), MOEA/D is found to be ineffective and inefficient, as uniformly distributed weight vectors used in decomposition approach cannot guarantee the even distribution of the optimal solutions on PFs. In this paper, an elite archive-based MOEA/D algorithm (ArchMOEA/D) is proposed to tackle the above problem. An external archive is used to store non-dominated solutions that help to spread the population diversity. Moreover, this external archive is evolved and used to compensate the search area that decomposition-based approaches cannot reach. The external archive and the main population cooperate with each other using Pareto- and decomposition-based techniques during the evolutionary process. Some experiments in solving benchmark problems with various properties have been used to verify the efficiency and effectiveness of ArchMOEA/D. Experimental results demonstrate the superior performance of ArchMOEA/D over other kinds of MOEA/D variants.
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
Lin, Q., Zhu, Q., Chen, J., et al.: A novel hybrid multi-objective immune algorithm with adaptive differential evolution. Comput. Oper. Res. 62, 95–111 (2015)
Deb, K.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
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). doi:10.1007/978-3-540-30217-9_84
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 12(2), 284–302 (2009)
Zhao, S., Suganthan, P., Zhang, Q.: Decomposition based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans. Evol. Comput. 16(3), 422–446 (2012)
Li, K., Fialho, A., Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for multiobjective evolutionary algorithm based decomposition. IEEE Trans. Evol. Comput. 19, 114–130 (2014)
Li, K., Kwong, S., Li, M., Wang, R.: Stable matching-based selection in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 18(6), 909–923 (2014)
Li, K., Kwong, S., Zhang, Q., Deb, K.: Inter-relationship based selection for decomposition multiobjective optimization. IEEE Trans. Cybern. 45(10), 2076–2088 (2015)
Li, H., Ding, M., Deng, J., Zhang, Q.: On the use of random weights in MOEA/D. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, pp. 978–985 (2015)
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). doi:10.1007/1-84628-137-7_6
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Schaffer, J.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100 (1985)
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)
Qi, T., et al.: MOEA/D with adaptive weight adjustment. Evol. Comput. 22(2), 231–264 (2014)
Li, K., Kwong, S., Deb, K.: A dual-population paradigm for evolutionary multiobjective optimization. Inf. Sci. 309, 50–72 (2015)
Lin, Q., et al.: A hybrid evolutionary immune algorithm for multiobjective optimization problems. IEEE Trans. Evol. Comput. 20(5), 711–729 (2016)
Wang, Z., Zhang, Q., Zhou, A., Gong, M., Jiao, L.: Adaptive replacement strategies for MOEA/D. IEEE Trans. Cybern. 46(2), 474–486 (2016)
Bosman, P., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 174–188 (2003)
Durillo, J., Nebro, A., Alba, E.: The jMetal framework for multi-objective optimization: design and architecture. In: Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, pp. 1–8 (2010)
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
Zhu, Q., Lin, Q., Chen, J. (2017). An Elite Archive-Based MOEA/D Algorithm. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-68759-9_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68758-2
Online ISBN: 978-3-319-68759-9
eBook Packages: Computer ScienceComputer Science (R0)