Skip to main content
SpringerLink
Log in

External archive matching strategy for MOEA/D

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a multiobjective optimization problem (MOP) into a group of subproblems and optimizes them at the same time. The reproduction method in MOEA/D, which generates offspring solutions, has crucial effect on the performance of algorithm. As the difficulties of MOPs increases, it requires much higher efficiency for the reproduction methods in MOEA/D. However, for the complex optimization problems whose PS shape is complicated, the original reproduction method used in MOEA/D might not be suitable to generate excellent offspring solutions. In order to improve the property of the reproduction method for MOEA/D, this paper proposes an external archive matching strategy which selects solutions’ most matching archive solutions as parent solutions. The offspring solutions generated by this strategy can maintain a good convergence ability. To balance convergence and diversity, a perturbed learning scheme is used to extend the search space of the solutions. The experimental results on three groups of test problems reveal that the solutions obtained by MOEA/D-EAM have better convergence and diversity than the other four state-of-the-art algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Alsmirat MA, Jararweh Y, Obaidat I, Gupta BB (2017) Automated wireless video surveillance: an evaluation framework. J Real Time Image Process 13(3):527–546

    Article  Google Scholar 

  • Alzain MA, Li AS, Soh B, Pardede E (2015) Multi-cloud data management using shamir’s secret sharing and quantum byzantine agreement schemes. Int J Cloud Appl Comput 5(3):35–52

    Google Scholar 

  • Beng KCT, Beng (2004) Multiobjective evolutionary algorithms and applications, vol 45, no 11. Springer, Berlin, pp 2281–2293

  • Beume N, Naujoks B, Emmerich M (2007) Sms-emoa: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653–1669

    Article  Google Scholar 

  • Cao Y, Zhou Z, Sun X, Gao C (2018) Coverless information hiding based on the molecular structure images of material. Comput Mater Contin 54(2):197–207

    Google Scholar 

  • Coello CAC, Corts NC (2005) Solving multiobjective optimization problems using an artificial immune system. Genet Program Evolv Mach 6(2):163–190

    Article  Google Scholar 

  • Deb K, Beyer HG (2001) Self-adaptive genetic algorithms with simulated binary crossover. Evol Comput 9(2):197–221

    Article  Google Scholar 

  • Deb K, Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken

    MATH  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. In: Evolutionary multiobjective optimization, pp 105–145

  • Gupta BB, Agrawal DP, Yamaguchi S (2016) Handbook of research on modern cryptographic solutions for computer and cyber security. IGI Global, Hershey

    Book  Google Scholar 

  • He P, Deng Z, Wang H, Liu Z (2016) Model approach to grammatical evolution: theory and case study. Soft Comput 20(9):3537–3548

    Article  Google Scholar 

  • Hillermeier C (2001) Nonlinear multiobjective optimization. Birkhäser Verlag, Basel

    Book  Google Scholar 

  • Huang Y, Li W, Liang Z, Xue Y, Wang X (2016) Efficient business process consolidation: combining topic features with structure matching. Soft Comput 22(2):645–657

    Article  Google Scholar 

  • Ishibuchi H, Sakane Y, Tsukamoto N, Nojima Y (2009) Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization. In: Eleventh conference on congress on evolutionary computation, pp 2508–2515

  • Ishibuchi H, Akedo N, Nojima Y (2013) Relation between neighborhood size and moea/d performance on manyobjective problems. In: International conference on evolutionary multi-criterion optimization, pp 459–474

    Google Scholar 

  • Lai X, Zou W, Xie D, Li X, Fan L (2017) DF relaying networks with randomly distributed interferers. IEEE Access 5:18909–18917

    Article  Google Scholar 

  • Li H, Zhang Q (2009) Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans Evol Comput 13(2):284–302

    Article  Google Scholar 

  • Li H, Ding M, Deng J, Zhang Q (2015) On the use of random weights in MOEA/D. In: IEEE Congress on Evolutionary Computation (CEC), pp 978–985

  • Li Y, Peng Z, Liang D, Chang H, Cai Z (2016) Facial age estimation by using stacked feature composition and selection. Vis Comput Int J Comput Graph 32(12):1525–1536

    Google Scholar 

  • Li P, Li J, Huang Z, Li T, Gao Cz, Chen WB, Chen K (2017a) Privacy-preserving outsourced classification in cloud computing. Cluster Comput 21(1):1–10

    Google Scholar 

  • Li P, Li J, Huang Z, Li T, Cz Gao, Yiu SM, Chen K (2017b) Multi-key privacy-preserving deep learning in cloud computing. Future Gener Comput Syst 74:76–85

    Article  Google Scholar 

  • Li W, Li K, Huang Y, Yang S, Yang L (2017c) A eaand aca-based qos multicast routing algorithm with multiple constraints for ad hoc networks. Soft Comput 21(19):5717–5727

    Article  Google Scholar 

  • Li W, Li S, Chen Z, Zhong L, Ouyang C (2017d) Self feedback differential evolution adapting to fitness landscape characteristics. Soft Comput. https://doi.org/10.1007/s00500-017-2833-y

    Article  Google Scholar 

  • Li W, Li K, Guo L, Huang Y, Xue Y (2018a) A new validity index adapted to fuzzy clustering algorithm. Multimed Tools Appl 77(3):1–23

    Article  Google Scholar 

  • Li Y, Wang G, Nie L, Wang Q, Tan W (2018b) Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recognit 75:51–62

    Article  Google Scholar 

  • Lin W, Xu S, He L, Li J (2017a) Multi-resource scheduling and power simulation for cloud computing. Inf Sci 397(C):168–186

    Article  Google Scholar 

  • Lin W, Xu SY, Li J, Xu L, Peng Z (2017b) Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput 21(5):1301–1314

    Article  Google Scholar 

  • Liu HL, Gu F, Zhang Q (2014) Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems. IEEE Trans Evol Comput 18(3):450–455

    Article  Google Scholar 

  • Qi Y, Ma X, Liu F, Jiao L, Sun J, Wu J (2014) MOEA/D with adaptive weight adjustment. Evol Comput 22(2):231–264

    Article  Google Scholar 

  • Sato H (2015) Analysis of inverted pbi and comparison with other scalarizing functions in decomposition based moeas. J Heuristics 21(6):819–849

    Article  Google Scholar 

  • Schutze O, Esquivel X, Lara A, Coello CAC (2012) Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans Evol Comput 16(4):504–522

    Article  Google Scholar 

  • Srinivas N, Deb K (2014) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  • Wang F, Zhang Y, Rao Q, Li K, Zhang H (2017a) Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction. Soft Comput 21(12):3193–3205

    Article  Google Scholar 

  • Wang Y, Li K, Li K (2017b) Partition scheduling on heterogeneous multicore processors for multi-dimensional loops applications. Int J Parallel Program 45:1–26

    Article  Google Scholar 

  • Wang Z, Zhang Q, Zhou A, Gong M, Jiao L (2017c) Adaptive replacement strategies for MOEA/D. IEEE Trans Cybern 46(2):474–486

    Article  Google Scholar 

  • Wang F, Zhang H, Li K, Lin Z, Yang J, Shen XL (2018a) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf Sci 436–437:162–177

    Article  MathSciNet  Google Scholar 

  • Wang H, Wang W, Cui Z, Zhou X, Zhao J, Li Y (2018b) A new dynamic firefly algorithm for demand estimation of water resources. Inf Sci 438:95–106

    Article  MathSciNet  Google Scholar 

  • Wu H, Kuang L, Wang F, Rao Q, Gong M, Li Y (2017) A multiobjective box-covering algorithm for fractal modularity on complex networks. Appl Soft Comput 61:294–313

    Article  Google Scholar 

  • Xie D, Lai X, Lei X, Fan L (2018) Cognitive multiuser energy harvesting decode-and-forward relaying system with direct links. IEEE Access 6:5596–5606

    Article  Google Scholar 

  • Yuan C, Li X, Wu QMJ, Li J, Sun X (2017) Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis. Comput Mater Contin 53(3):357–371

    Google Scholar 

  • Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  • Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2008) Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester

    Google Scholar 

  • Zhang H, Zhou A, Song S, Zhang Q, Gao XZ, Zhang J (2016) A self-organizing multiobjective evolutionary algorithm. IEEE Trans Evol Comput 20(5):792–806

    Article  Google Scholar 

  • Zhang S, Yang Z, Xing X, Gao Y, Xie D, Wong HS (2017) Generalized pair-counting similarity measures for clustering and cluster ensembles. IEEE Access 5:16904–16918

    Article  Google Scholar 

  • Zhou J, Wang F, Xu J, Yan Y, Zhu H (2018) A novel character segmentation method for serial number on banknotes with complex background. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0707-5

    Article  Google Scholar 

  • Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Nature Science Foundation of China (Grant No. 61773296); the 111 Programme of Introducing Talents of Discipline to Universities (Grant No. B07037); the Fundamental Research Funds for the Central Universities (Grant No. 2042018kf0224); and Research Fund for Academic Team of Young Scholars at Wuhan University (Grant No. Whu2016013).

Author information

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Feng Wang, Heng Zhang, Yixuan Li & Yaoyu Zhao

  2. Institute of Computational Linguistics, Peking University, Beijing, China

    Qi Rao

Authors
  1. Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yixuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaoyu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qi Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Rao.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by B. B. Gupta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, F., Zhang, H., Li, Y. et al. External archive matching strategy for MOEA/D. Soft Comput 22, 7833–7846 (2018). https://doi.org/10.1007/s00500-018-3499-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3499-9

Keywords

Search

Navigation