Skip to main content
Log in

A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Harris Hawks Optimization is a recently proposed algorithm inspired by the cooperative manner and chasing behavior of harris. However, from the experimental results, it can be noticed that HHO may fall in local optima or have a slow convergence curve in some complex optimization tasks. In this paper, an improved version of HHO called IHHO is proposed which enhances the performance of HHO by combining HHO with opposition-based learning (OBL), Chaotic Local Search (CLS), and a self-adaptive technique. In order to show the performance of the proposed algorithm, several experiments are conducted using the Standard IEEE CEC 2017 benchmark. IHHO is compared with the classical HHO and other 10 state-of-art algorithms. Moreover, IHHO is used to solve 5 constrained engineering problems. IHHO has also been applied to solve feature selection problem using 7 UCI dataset. The numerical results and analysis show the superiority of IHHO in solving real-world problems.

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

Access this article

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Abdel AS, Zobaa A, Balci M, Ismail S (2019) Harmonic overloading minimization of frequency-dependent components in harmonics polluted distribution systems using Harris hawks optimization algorithm. IEEE:100824–100837

  2. Abualigah L, Abd EM, Hussien AG, Alsalibie B, Jafar SMJ, Gandomi AH (2020) Lightning search algorithm: a comprehensive survey. Appl Intell 51:2353–2376

  3. Alabool HM, Alarabiat D, Abualigah L, Heidari AA (2021) Harris hawks optimization: a comprehensive review of recent variants and applications. Neural Comput Appl:1–42

  4. Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37(8):5682–5687

    Article  Google Scholar 

  5. Antoniou A (2016) Digital signal processing. McGraw-Hill, New York

    Google Scholar 

  6. Antoniou A, Lu W-S (2007) Practical optimization: algorithms and engineering applications. Springer, New York

    MATH  Google Scholar 

  7. Assiri AS, Hussien AG, Amin M (2020) Ant lion optimization: variants, hybrids, and applications. IEEE Access 8:77746–77764

    Article  Google Scholar 

  8. Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2016) An ensemble sinusoidal parameter adaptation incorporated with l-shade for solving cec2014 benchmark problems. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 2958–2965

  9. Bao X, Jia H, Lang C (2019) A novel hybrid Harris hawks optimization for color image multilevel thresholding segmentation. IEEE Access 7:76529–76546

    Article  Google Scholar 

  10. Bednarz JC (1988) Cooperative hunting harris’ hawks (parabuteo unicinctus). Science 239(4847):1525–1527

    Article  Google Scholar 

  11. Chen H, Heidari AA, Chen H, Wang M, Pan Z, Gandomi AH (2020a) Multi-population differential evolution-assisted harris hawks optimization: framework and case studies. Futur Gener Comput Syst 111:175–198

    Article  Google Scholar 

  12. Chen Z, Zhang L, Tian G, Nasr EA (2020b) Economic maintenance planning of complex systems based on discrete artificial bee colony algorithm. IEEE Access 8:108062–108071

    Article  Google Scholar 

  13. Cuevas E, Cienfuegos M, ZaldíVar D, Pérez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40(16):6374–6384

    Article  Google Scholar 

  14. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  15. dos Santos Coelho L, Mariani VC (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34(3):1905–1913

    Article  Google Scholar 

  16. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science. IEEE, pp 39–43

  17. Elaziz MA, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500

    Article  Google Scholar 

  18. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  19. Ewees AA, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Article  Google Scholar 

  20. Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55

    Article  Google Scholar 

  21. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  22. Golilarz NA, Gao H, Demirel H (2019) Satellite image de-noising with Harris hawks meta heuristic optimization algorithm and improved adaptive generalized gaussian distribution threshold function. IEEE Access 7:57459–57468

    Article  Google Scholar 

  23. Gupta S, Deep K (2019a) An efficient grey wolf optimizer with opposition-based learning and chaotic local search for integer and mixed-integer optimization problems. Arab J Sci Eng:1–20

  24. Gupta S, Deep K (2019b) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230

  25. Gupta S, Deep K (2019c) Improved sine cosine algorithm with crossover scheme for global optimization. Knowl Based Syst 165:374–406

    Article  Google Scholar 

  26. He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186(2):1407–1422

    MathSciNet  MATH  Google Scholar 

  27. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  28. Holland JH et al (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, New York

    Book  Google Scholar 

  29. Houssein EH, Hosney ME, Elhoseny M, Oliva D, Mohamed WM, Hassaballah M (2020) Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics. Sci Rep 10(1):1–22

    Article  Google Scholar 

  30. Hussain K, Neggaz N, Zhu W, Houssein EH (2021) An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection. Expert Syst Appl 176:114778

  31. Hussien AG, Amin M, Abd El Aziz M (2020a) A comprehensive review of moth-flame optimisation: variants, hybrids, and applications. J Exp Theor Artif Intell 32(4):705–725

  32. Hussien AG, Amin M, Wang M, Liang G, Alsanad A, Gumaei A, Chen H (2020b) Crow search algorithm: theory, recent advances, and applications. IEEE Access 8:173548–173565

    Article  Google Scholar 

  33. Hussien AG, Oliva D, Houssein Houssein EH, Juanand AA, Yu X (2020c) Binary whale optimization algorithm for dimensionality reduction. Mathematics 8(10):1821

  34. Hussien AG, Hassanien AE, Houssein EH (2017a) Swarming behaviour of salps algorithm for predicting chemical compound activities. In: 2017 Eighth international conference on intelligent computing and information systems (ICICIS). IEEE, pp 315–320

  35. Hussien AG, Houssein EH, Hassanien AE (2017b) A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection. In: 2017 Eighth international conference on intelligent computing and information systems (ICICIS). IEEE, pp 166–172

  36. Hussien AG, Hassanien AE, Houssein EH, Amin M, Azar AT (2019a) New binary whale optimization algorithm for discrete optimization problems. Eng Optim 52(6):1–15

  37. Hussien AG, Hassanien AE, Houssein EH, Bhattacharyya S, Amin M (2019b) S-shaped binary whale optimization algorithm for feature selection. In: Recent trends in signal and image processing. Springer, pp 79–87

  38. Ibrahim RA, Elaziz MA, Lu S (2018) Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Syst Appl 108:1–27

    Article  Google Scholar 

  39. Jia D, Zheng G, Khan MK (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181(15):3175–3187

    Article  Google Scholar 

  40. Jia H, Lang C, Oliva D, Song W, Peng X (2019) Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens 11(12):1421

    Article  Google Scholar 

  41. Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018

    Article  Google Scholar 

  42. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  43. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294

    Article  Google Scholar 

  44. Koziel S, Leifsson L, Yang X-S (2014) Solving computationally expensive engineering problems: methods and applications, vol 97. Springer, New York

    Book  Google Scholar 

  45. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82(9–10):781–798

    Article  Google Scholar 

  46. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640

    Article  Google Scholar 

  47. Mirjalili S (2015a) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

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

    Article  Google Scholar 

  49. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  50. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

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

    Article  Google Scholar 

  52. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  53. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  54. Moayedi H, Abdullahi MM, Nguyen H, Rashid ASA (2019a) Comparison of dragonfly algorithm and harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils. Eng Comput:1–11

  55. Moayedi H, Osouli A, Nguyen H, Rashid ASA (2019b) A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Eng Comput:1–11

  56. Neggaz N, Ewees AA, Abd Elaziz M, Mafarja M (2020a) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103

    Article  Google Scholar 

  57. Neggaz N, Houssein EH, Hussain K (2020b) An efficient henry gas solubility optimization for feature selection. Expert Syst Appl 152:113364

    Article  Google Scholar 

  58. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCAIAWTIC'06), vol 1. IEEE, pp. 695–701

  59. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67

    Article  Google Scholar 

  60. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  61. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612

    Article  Google Scholar 

  62. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229

    Article  Google Scholar 

  63. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  64. Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171–188

    Article  Google Scholar 

  65. Singla M, Ghosh D, Shukla K (2019) A survey of robust optimization based machine learning with special reference to support vector machines. Int J Mach Learn Cybern:1–27

  66. Song S, Wang P, Heidari AA, Wang M, Zhao X, Chen H, He W, Xu S (2021) Dimension decided Harris hawks optimization with Gaussian mutation: balance analysis and diversity patterns. Knowl Based Syst 215:106425

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  68. Tanabe R, Fukunaga AS (2014) Improving the search performance of shade using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1658–1665

  69. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), vol 1. IEEE, pp 695–701

  70. Wang W, Tian G, Chen M, Tao F, Zhang C, Abdulraham A-A, Li Z, Jiang Z (2020) Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints. J Clean Prod 245:118714

    Article  Google Scholar 

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

    Article  Google Scholar 

  72. Wu G, Mallipeddi R, Suganthan P (2017) Problem definitions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report

  73. Xu Y, Chen H, Heidari AA, Luo J, Zhang Q, Zhao X, Li C (2019) An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Syst Appl 129:135–155

    Article  Google Scholar 

  74. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074

    Article  Google Scholar 

  75. Zhou Y, Luo Q, Chen H, He A, Wu J (2015) A discrete invasive weed optimization algorithm for solving traveling salesman problem. Neurocomputing 151:1227–1236

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abdelazim G. Hussien.

Ethics declarations

Conflict of interest

The 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

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

Hussien, A.G., Amin, M. A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. Int. J. Mach. Learn. & Cyber. 13, 309–336 (2022). https://doi.org/10.1007/s13042-021-01326-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01326-4

Keywords

Navigation