Skip to main content
SpringerLink
Log in

Hybrid many-objective cuckoo search algorithm with Lévy and exponential distributions

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Hybrid many-objective cuckoo search algorithm (HMaOCS) is a newly proposed method for Many-objective optimization problems (MaOPs), and has achieved promising performance. However, Lévy and Gaussian distributions used in global search manner of HMaOCS is originally proposed for optimization problems with one objective, and they are not suitable for MaOPs as illustrated in this paper. To further exploit the potential of HMaOCS, this paper investigates four different probability distributions and their six corresponding combinations. Comparison results illustrate that the combination of Lévy and Exponential distributions is able to greatly improve HMaOCS. On the basis of comparison results and analysis on both DTLZ and WFG test suites with 2, 3, 4, 6, 8 and 10 objectives, it can be concluded that HMaOCS with Lévy and Exponential distributions exhibits better performance compared with most advanced 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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Zhang M, Wang H, Cui Z, Chen J (2018) Hybrid multi-objective cuckoo search with dynamical local search. Memet Comput 10(2):199–208

    Article  Google Scholar 

  2. Rostami S, Neri F, Epitropakis M (2017) Progressive preference articulation for decision making in multi-objective optimisation problems. Integr Comput-Aided Eng 24(4):315–335

    Article  Google Scholar 

  3. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197

    Article  Google Scholar 

  4. Coello Coello C A, Lechuga M S(2002) MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 1051–1056

  5. Rostami S, Neri F (2016) Covariance matrix adaptation pareto archived evolution strategy with hyper volume-sorted adaptive grid algorithm. Integr Comput Aided Eng 23(4):313–329

    Article  Google Scholar 

  6. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: Solving problems with box constraints. IEEE Trans Evolut Comput 18(4):577–601

    Article  Google Scholar 

  7. Rostami S, Neri F (2017) A fast hypervolume driven selection mechanism for many-objective optimisation problems. Swarm Evolut Comput 34:50–67

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Yang S, Li M, Liu X, Zheng J (2013) A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans Evolut Comput 17(5):721–736

    Article  Google Scholar 

  10. Zhang X, Tian Y, Jin Y (2015) A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans Evolut Comput 19(6):761–776

    Article  Google Scholar 

  11. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multi-objective optimization. MIT Press, Cambridge, pp 263–282

    Google Scholar 

  12. Zou X, Chen Y, Liu M, Kang L (2008) A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans Syst Man Cybern Part B Cybern 38(5):1402–1412

    Article  Google Scholar 

  13. Zitzler E, Kunzli S (2004) Indicator-based selection in multiobjective search. In: Proceedings of the international conference on parallel problem solving from nature, pp 832–842

  14. Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evolut Comput 19(1):45–76

    Article  Google Scholar 

  15. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evolut Comput 3(4):257–271

    Article  Google Scholar 

  16. Nebro A J, Durillo J J, Garcia-Nieto J, Coello Coello CA, Luna F, Alba E (2009) SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: Proceedings of the IEEE symposium on computational intelligence in multi-criteria decision-making, pp 66–73

  17. Martinez SZ, Coello Coello CA (2011) A multi-objective particle swarm optimizer based on decomposition. In: Proceedings of the 13th annual conference on genetic and evolutionary computation, pp 69–76

  18. Okabe T, Jin Y, Sendhoff B (2004) Voronoi-based estimation of distribution algorithm for multi-objective optimization. In: IEEE congress on evolutionary computation 2004

  19. Bosman PAN, Thierens D (2005) The naive MIDEA: a baseline multi-objective EA. In: Proceedings of the third international conference on evolutionary multi-criterion optimization. Springer-Verlag

  20. Zhang Q, Zhou A, Jin Y (2008) RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans Evolut Comput 12(1):41–63

    Article  Google Scholar 

  21. Yang XS, Deb S (2010) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing, India, pp 210–214

  22. Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evolut Comput 5:1–12

    Article  Google Scholar 

  23. Wu Z, Zhao X, Ma Y, Zhao Y (2019) A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting. Appl Energy. https://doi.org/10.1016/j.apenergy.2019.01.046

    Article  Google Scholar 

  24. Ruining Z, Xuemei J, Ruifang L (2018) Improved decomposition-based multi-objective cuckoo search algorithm for spectrum allocation in cognitive vehicular network. Phys Commun 2018:S1874490717303506

    Google Scholar 

  25. Cui Z, Zhang M, Wang H, Cai X, Zhang W (2019) A hybrid many-objective Cuckoo search algorithm. Soft Comput. https://doi.org/10.1007/s00500-019-04004-4

    Article  Google Scholar 

  26. Balasubbareddya M, Sivanagarajub S, Venkata Sureshc C (2017) A non-dominated Sorting Hybrid Cuckoo Search Algorithm for multi-objective optimization in the presence of FACTS devices. Rus Electr Eng 88(1):44–53

    Article  Google Scholar 

  27. Wang Z, Li Y (2015) Irreversibility analysis for optimization design of plate fin heat exchangers using a multi-objective cuckoo search algorithm. Energy Convers Manag 101:126–135

    Article  Google Scholar 

  28. Xiong W, Guo B, Shen Y (2018) A Discrete multi-objective optimization method for hardware/software partitioning problem based on cuckoo search and elite strategy. Neuroquantology 16(5):749–756

    Article  Google Scholar 

  29. Cui Z, Sun B, Wang G (2016) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distrib Comput:S0743731516301393

Download references

Funding

This paper is supported by National Natural Science Foundation of China under Grant Nos. 61806138, U1636220, 61663028, 71771176, 51775385, 61703279 and 71371142, Natural Science Foundation of Shanxi Province under Grant No. 201801D121127, PhD Research Startup Foundation of Taiyuan University of Science and Technology under Grant No. 20182002, the Distinguished Young Talents Plan of Jiang-xi Province under Grant No. 20171BCB23075, the Natural Science Foundation of Jiang-xi Province under Grant No. 20171BAB202035.

Author information

Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China

    Zhihua Cui & Xingjuan Cai

  2. School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Maoqing Zhang

  3. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Hui Wang

  4. Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China

    Wensheng Zhang

  5. Swinburne University of Technology, Melbourne, 3000, Australia

    Jinjun Chen

Authors
  1. Zhihua Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maoqing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingjuan Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wensheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinjun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maoqing Zhang.

Ethics declarations

Conflict of interest

Authors Zhihua Cui, Maoqing Zhang, Hui Wang, Xingjuan Cai, Wensheng Zhang, Jinjun Chen 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.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, Z., Zhang, M., Wang, H. et al. Hybrid many-objective cuckoo search algorithm with Lévy and exponential distributions. Memetic Comp. 12, 251–265 (2020). https://doi.org/10.1007/s12293-020-00308-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-020-00308-3

Keywords

Search

Navigation