Skip to main content
SpringerLink
Log in

A hybrid many-objective cuckoo search algorithm

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

Abstract

Cuckoo search (CS) is an excellent population-based algorithm and has shown promising performance in dealing with single- and multi-objective optimization problems. However, for many-objective optimization problems (MaOPs), CS cannot be directly employed. So far, few paper have been reported to use CS to solve MaOPs. In this paper, we try to propose a hybrid many-objective cuckoo search (HMaOCS) for MaOPs. In HMaOCS, the standard CS is firstly modified to effectively deal with MaOPs. Then, non-dominated sorting and the strategy of reference points are employed to ensure the convergence and diversity. In order to verify the performance of HMaOCS, DTLZ and WFG benchmark sets are utilized in the experiments. Experimental results show that HMaOCS can achieve promising performance compared with five other well-known many-objective optimization 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

Similar content being viewed by others

References

  • Abdel-Baset M, Zhou Y, Ismail M (2018) An improved cuckoo search algorithm for integer programming problems. Int J Comput Sci Math 9(1):66–81

    Article  MathSciNet  Google Scholar 

  • Adra S, Fleming P (2011) Diversity management in evolutionary many-objective optimization. IEEE Trans Evol Comput 15(2):183–195

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Barthelemy P, Bertolotti J, Wiersma D (2008) A Lévy flight for light. Nature 453(7194):495

    Google Scholar 

  • Cai X, Gao X, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-inspired Comput 8(4):205–214

    Article  Google Scholar 

  • Cai X, Wang H, Cui Z, Cai J, Xue Y, Wang L (2018) Bat algorithm with triangle-flipping strategy for numerical optimization. Int J Mach Learn Cybernet 9(2):199–215

    Article  Google Scholar 

  • Chandrasekaran K, Simon S (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol Comput 5:1–16

    Article  Google Scholar 

  • Coelho L, Guerra F, Batistela N (2013) Multiobjective cuckoo search algorithm based on duffing’s oscillator applied to jiles-atherton vector hysteresis parameters estimation. IEEE Trans Magn 49(5):1745–1748

    Article  Google Scholar 

  • Cortés P, Muñuzuri J, Onieva L, Guadix J (2018) A discrete particle swarm optimisation algorithm to operate distributed energy generation networks efficiently. Int J Bio-Inspired Comput 12(4):226–235

    Article  Google Scholar 

  • Cui Z, Cao Y, Cai X, Cai J, Chen J (2017a) Optimal LEACH protocol with modified bat algorithm for big data sensing systems in internet of things. J Parallel Distrib Comput 10:1–12. https://doi.org/10.1016/j.jpdc.2017.12.014

    Article  Google Scholar 

  • Cui Z, Sun B, Wang G, Xue Y, Chen J (2017b) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distrib Comput 103:42–52

    Article  Google Scholar 

  • Cui Z, Xue F, Cai X, Cao Y, Wang G, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Industr Inf 14(7):3187–3196

    Article  Google Scholar 

  • Cui Z, Du L, Wang P, Cai X, Zhang W (2019a) Malicious code detection based on CNNs and multi-objective algorithm. J Parallel Distrib Comput 129:50–58

    Article  Google Scholar 

  • Cui Z, Li F, Zhang W (2019b) Bat algorithm with principal component analysis. Int J Mach Learn Cybernet 10(3):603–622

    Article  Google Scholar 

  • Cui Z, Zhang J, Wang Y, Cao Y, Cai X, Zhang W, Chen J (2019c) A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci China Inf Sci 62(7):070212. https://doi.org/10.1007/s11432-018-9729-5

    Article  Google Scholar 

  • Das I, Dennis J (2006) Normal-Boundary Intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J Optim 8(3):631–657

    Article  MathSciNet  MATH  Google Scholar 

  • Deb K, Jain H (2014) 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

    Article  Google Scholar 

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

    MATH  Google Scholar 

  • Deb K, Thiele L, Laumanns M, Zitzler E (2002a) Scalable multi-objective optimization test problems. In: Proceedings of the 2002 congress on IEEE evolutionary computation. CEC ‘02, pp 825–830

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

    Article  Google Scholar 

  • Deb K, Mohan M, Mishra S (2005) Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol Comput 13(4):501–525

    Article  Google Scholar 

  • Fan J, Li Y, Tang L, Wu G (2018) RoughPSO: rough set-based particle swarm optimization. Int J Bio-Inspired Comput 12(4):245–253

    Article  Google Scholar 

  • Hanoun S, Nahavandi S, Creighton D, Kull H (2012) Solving a multiobjective job shop scheduling problem using Pareto archived cuckoo search. Emerg Technol Factory Autom IEEE 43:1–8

    Google Scholar 

  • Huband S, Hingston P, Barone L, While L (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans Evol Comput 10(5):477–506

    Article  MATH  Google Scholar 

  • Hughes E (2003) Multiple single objective Pareto sampling. In: The 2003 congress on IEEE evolutionary computation (CEC), vol 4, pp 2678–2684

  • Jain H, Deb K (2014) 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

    Article  Google Scholar 

  • 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 

  • Li M, Zheng J (2009) Spread assessment for evolutionary multi-objective optimization. In: International conference on evolutionary multi-criterion optimization, Springer, pp 216–230

  • Li M, Zheng J, Li K, Yuan Q, Shen R (2010) Enhancing diversity for average ranking method in evolutionary many-objective optimization. In: Parallel problem solving from nature, PPSN XI. Springer, Berlin, pp. 647–656

  • Li M, Yang S, Liu X (2014) Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Trans Evol Comput 18(3):348–365

    Article  Google Scholar 

  • Niu Y, Tian Z, Zhang M, Cai X, Li J (2018) Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction. Int J Comput Sci Math 9(6):547–554

    Article  MathSciNet  Google Scholar 

  • Pandey H, Chaudhary A, Mehrotra D (2018) Bit mask-oriented genetic algorithm for grammatical inference and premature convergence. Int J Bio-Inspired Comput 12(1):54–69

    Article  Google Scholar 

  • Pooja P, Chaturvedi P, Kumar P, Tomar A (2018) A novel differential evolution approach for constraint optimization. Int J Bio-Inspired Comput 12(4):254–265

    Article  Google Scholar 

  • Raja B, Jhala R, Patel V (2017) Many-objective optimization of cross-flow plate-fin heat exchanger. Int J Therm Sci 118:320–339

    Article  Google Scholar 

  • Rani K, Malek M, Neoh S (2013) Hybrid multiobjective optimization using modified cuckoo search algorithm in linear array synthesis. In: IEEE antennas and propagation conference, pp 1–4

  • Reynolds AM, Frye MA (2007) Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS ONE 2(4):e354

    Article  Google Scholar 

  • Shan X, Ye B, Zhang L (2018) Analysis of flow field of hydrodynamic suspension polishing disk based on multi-fractal method. Int J Comput Sci Math 9(1):13–20

    Article  MathSciNet  Google Scholar 

  • Sun B, Cui Z, Dai C (2014) DV-hop localization algorithm with cuckoo search. Sensor Lett 12(2):444–447

    Article  Google Scholar 

  • Tozer B, Mazzuchi T, Sarkani S (2017) Many-objective stochastic path finding using reinforcement learning. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.10.045

    Article  Google Scholar 

  • 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 

  • Wang Q, Liu S, Wang H (2012) Multi-objective cuckoo search for the optimal design of water distribution systems. In: International conference on civil engineering and urban planning. https://doi.org/10.1061/9780784412435.072

  • Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X, Cui Z (2017) Firefly algorithm with neighborhood attraction. Inf Sci 382(383):374–387

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Yang X, Deb S (2010a) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing 2009, NaBIC 2009, world congress on IEEE, pp 210–214

  • Yang X, Deb S (2010b) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

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

    Article  Google Scholar 

  • Yigit T, Unsal O, Deperlioglu O (2018) Using the metaheuristic methods for real-time optimisation of dynamic school bus routing problem and an application. Int J Bio-Inspired Comput 11(2):123–133

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 

  • Zhao B, Xue Y, Xu B, Ma T, Liu J (2018) Multi-objective classification based on NSGA-II. Int J Comput Sci Math 9(6):539–546

    Article  MathSciNet  Google Scholar 

  • Zhou X, Liu Y, Li B (2016) A multi-objective discrete cuckoo search algorithm with local search for community detection in complex networks. Mod Phys Lett B 30(07):1650080

    Article  MathSciNet  Google Scholar 

  • Zitzler E, Kunzli S (2004) Indicator-based selection in multi objective search. In: Lecture notes in computing science, pp 832–842

  • Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou K, et al. (eds) EUROGEN 2001. International Center for Numerical Methods in engineering (CIMNE), pp 95–100

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

    Article  Google Scholar 

Download references

Funding

This study is funded by the 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, 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

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

Corresponding authors

Correspondence to Maoqing Zhang or Xingjuan Cai.

Ethics declarations

Conflict of interest

Author Zhihua Cui declares that he has no conflict of interest. Author Maoqing Zhang declares that he has no conflict of interest. Author Hui Wang declares that he has no conflict of interest. Author Xingjuan Cai declares that she has no conflict of interest. Author Wensheng Zhang declares that he has 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

Communicated by A. Di Nola.

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. A hybrid many-objective cuckoo search algorithm. Soft Comput 23, 10681–10697 (2019). https://doi.org/10.1007/s00500-019-04004-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04004-4

Keywords

Search

Navigation