Skip to main content
SpringerLink
Log in

Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In recent years, cuckoo search (CS) algorithm has been successfully applied in single-objective optimization problems. In addition, decomposition-based multi-objective evolutionary algorithms (MOEA/D) have high performance for multi-objective optimization problems (MOPs). Inspired by this, a new decomposition-based multi-objective CS algorithm is proposed in this paper. Two reproduction operators with different characteristics derived from the CS algorithm are constructed and they compose an operator pool. Then, a bandit-based adaptive operator selection method is used to determine the application of different operators. An angle-based selection strategy that achieves a better balance between convergence and diversity is adopted to preserve diversity. Compared with other improved strategies designed for MOEA/D on two suits of test instances, the proposed algorithm was demonstrated to be effective and competitive for MOPs.

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

Similar content being viewed by others

References

  1. Cai X, Yang Z, Fan Z, Zhang Q (2016) Decomposition-based-sorting and angle-based-selection for evolutionary multiobjective and many-objective optimization. IEEE Trans Cybern 47(9):2824–2837

    Google Scholar 

  2. Cao Y, Ding ZM, Xue F, Rong XT (2018) An improved twin support vector machine based on multi-objective cuckoo search for software defect prediction. Int J Bio-Inspired Comput 11(4):282–291

    Google Scholar 

  3. Civicioglu P, Besdok E (2013) A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346

    Google Scholar 

  4. Coello CAC, Cortés NC (2005) Solving multiobjective optimization problems using an artificial immune system. Gen Programm Evol Mach 6(2):163–190

    Google Scholar 

  5. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. Ieee Trans Evol Comput 8(3):256–279

    Google Scholar 

  6. Deb K (2001) Multi-objective optimization using evolutionary algorithms, vol 16. Wiley, New York

  7. Deb K (2008) Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization. Eur J Oper Res 185(3):1062–1087

    MathSciNet  MATH  Google Scholar 

  8. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Compl Syst 9(3):115–148

    MathSciNet  MATH  Google Scholar 

  9. Deb K, Anand A, Joshi D (2002) A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput 10(4):371–395

    Google Scholar 

  10. Deb K, Goyal M A combined genetic adaptive search (geneas) for engineering design

  11. Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Google Scholar 

  12. Goldberg DE (1990) Probability matching, the magnitude of reinforcement, and classifier system bidding. Mach Learn 5(4):407–425

    Google Scholar 

  13. Goncalves R, et al. (2018) A New Hyper-Heuristic based on a Contextual Multi-Armed Bandit for Many-Objective Optimization. IEEE Congress on Evolutionary Computation. 2018. 997–1004. https://ieeexplore.ieee.org/document/8477930.

  14. Goncalves RA, de Almeida CP, Venske SMGS, Delgado MRdBdS, Pozo ATR (2017) A new hyper-heuristic based on a restless multi-armed bandit for multi-objective optimization, pp 390–395

  15. Gong M, Jiao L, Du H, Bo L (2014) Multiobjective immune algorithm with nondominated neighbor-based selection. Evol Comput 16(2):225–255

    Google Scholar 

  16. Higuchi T, Tsutsui S, Yamamura M (2001) Simplex crossover for real-coded genetic algolithms. Trans Jpn Soc Artif Intell 16(1):147–155

    Google Scholar 

  17. Hitomi N, Selva D (2017) A classification and comparison of credit assignment strategies in multiobjective adaptive operator selection. IEEE Trans Evol Comput 21(2):294–314

    Google Scholar 

  18. 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

    MATH  Google Scholar 

  19. Jiang S, Yang S, Wang Y, Liu X (2018) Scalarizing functions in decomposition-based multiobjective evolutionary algorithms. IEEE Trans Evol Comput 22(2):296–313

    Google Scholar 

  20. Kennedy J, Eberhart R Particle swarm optimization. In: Icnn95-international Conference on Neural Networks

  21. 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

    Google Scholar 

  22. Li K, Fialho A, Kwong S, Zhang Q (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 18(1):114– 130

    Google Scholar 

  23. Li K, Zhang Q, Kwong S, Li M, Wang R (2014) Stable matching based selection in evolutionary multiobjective optimization. IEEE Trans Evol Comput 18(6):909–923

    Google Scholar 

  24. Liagkouras K, Metaxiotis K An elitist polynomial mutation operator for improved performance of moeas in computer networks. In: International Conference on Computer Communications & Networks

  25. Lin Q, Liu Z, Yan Q, Du Z, Coello CAC, Liang Z, Wang W, Chen J (2016) Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inf Sci 339:332–352

    Google Scholar 

  26. Ma X, Zhang Q, Tian GD, Yang JS, Zhu ZX (2018) On tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Trans Evol Comput 22(2):226–244

    Google Scholar 

  27. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Google Scholar 

  28. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  29. Nakanishi H, Kinjo H, Oshiro N, Yamamoto T (2007) Searching performance of a real-coded genetic algorithm using biased probability distribution functions and mutation. Artif Life Robot 11(1)

  30. Nguyen TT, Vo DN (2017) Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling. Swarm Evol Comput 37:73–89

    Google Scholar 

  31. Pierre DM, Zakaria N (2017) Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows. Appl Soft Comput 52:863–876

    Google Scholar 

  32. 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

    Google Scholar 

  33. Qiu X, Xu JX, Tan KC, Abbass HA (2016) Adaptive cross-generation differential evolution operators for multiobjective optimization. IEEE Trans Evol Comput 20(2):232–244

    Google Scholar 

  34. Savsani V, Tawhid MA (2017) Non-dominated sorting moth flame optimization (ns-mfo) for multi-objective problems. Eng Appl Artif Intell 63:20–32

    Google Scholar 

  35. Sindhya K, Ruuska S, Haanpaa T, Miettinen K (2011) A new hybrid mutation operator for multiobjective optimization with differential evolution. Soft Comput 15(10):2041–2055

    Google Scholar 

  36. Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  37. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  38. Tarakanov A, Dasgupta D (2000) A formal model of an artificial immune system. Biosystems 55(1-3):151–158

    Google Scholar 

  39. Thierens D (2007) Adaptive strategies for operator allocation. Parameter Sett Evol Algorithm 54:77–90

    Google Scholar 

  40. Tian Y, Cheng R, Zhang X, Jin Y (2017) Platemo: a matlab platform for evolutionary multi-objective optimization. IEEE Comput Intell Mag 12(4):73–87

    Google Scholar 

  41. Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci USA 104(3):708–711

    Google Scholar 

  42. Wang JZ, Du P, Niu T, Yang WD (2017) A novel hybrid system based on a new proposed algorithm-multi-objective whale optimization algorithm for wind speed forecasting. Appl Energy 208:344–360

    Google Scholar 

  43. Wang R, Zhang Q, Zhang T (2016) Decomposition based algorithms using pareto adaptive scalarizing methods. IEEE Trans Evol Comput 20(6):821–837

    Google Scholar 

  44. Wang Z, Zhang Q, Li H Balancing convergence and diversity by using two different reproduction operators in moea/d: Some preliminary work. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 2849–2854

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

    Google Scholar 

  46. Wu M, Ke L, Kwong S, Zhang Q, Zhang J (2018) Learning to decompose: a paradigm for decomposition-based multiobjective optimization. IEEE Trans Evol Comput PP(99):1–1

    Google Scholar 

  47. Yagmahan B, Yenisey MM (2008) Ant colony optimization for multi-objective flow shop scheduling problem. Comput Ind Eng 54(3):411–420

    Google Scholar 

  48. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    MathSciNet  MATH  Google Scholar 

  49. Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174

    Google Scholar 

  50. Yuan Y, Xu H, Wang B, Zhang B, Yao X (2016) Balancing convergence and diversity in decomposition-based many-objective optimizers. IEEE Trans Evol Comput 20(2):180–198

    Google Scholar 

  51. Zalik KR, Zalik B (2018) Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks. Neural Comput Appl 30(9):2907–2920

    Google Scholar 

  52. Zhang Q, Liu W, Li H The performance of a new version of moea/d. In: 2009. CEC ’09. IEEE Congress on Evolutionary Computation, pp 203–208

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

    Google Scholar 

  54. Zhao SZ, Suganthan PN, Zhang Q (2012) Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans Evol Comput 16(3):442–446

    Google Scholar 

  55. Zhou JJ, Yao XF (2017) A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. Int J Prod Res 55(16):4765–4784

    Google Scholar 

  56. Zhu QL, Lin QZ, Du ZH, Liang ZP, Wang WJ, Zhu ZX, Chen JY, Huang PZ, Ming Z (2016) A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm. Inf Sci 345:177–198

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  59. Zitzler E, Thiele L, Laumanns M, Fonseca CM, Fonseca VGD (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. No.1, Haifu Lane, Guanghua Road, Qinhuai District, Nanjing City, Jiangsu Prov, China

    Liang Chen, Wenyan Gan, Hongwei Li & Kai Cheng

  2. No.1155, Yanshan Road, Yuhui District, Bengbu City, Anhui Prov, China

    Liang Chen & Zili Zhang

  3. No. 1 Hongjing Avenue, Jiangning Science Park, Nanjing City, Jiangsu Prov, China

    Darong Pan & Li Chen

Authors
  1. Liang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenyan Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kai Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Darong Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zili Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Chen.

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

Chen, L., Gan, W., Li, H. et al. Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition. Appl Intell 51, 143–160 (2021). https://doi.org/10.1007/s10489-020-01816-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01816-y

Keywords

Search

Navigation