Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Effective hybridization of JAYA and teaching–learning-based optimization algorithms for numerical function optimization

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

Abstract

The JAYA is classified as the state-of-the-art population-oriented algorithm for the optimization of diverse problems, both discrete and continuous. The concept behind this algorithm is to present a solution by means of the best and worst individuals in the population. On the other hand, teaching–learning-based optimization algorithm cooperation of a teacher on students’ learning process. Due to each one having some benefits and drawbacks, combining those leads to better exploring the problem. Consequently, this investigation exploits the hybridization of both mentioned algorithms, and a novel algorithm is made named H-JTLBO (hybridization of JAYA and teaching learning-based optimization). The proposed approach is then evaluated using different test functions used frequently in the literate. Finally, the results of such functions are compared with other optimization algorithms which have recently been introduced in the literature, such as Sine Cosine Algorithm (SCA), Grasshopper Optimization Algorithm (GOA), Moth-flame optimization (MFO), and JAYA algorithm. In addition, the statistical test is used to evaluate the proposed method. Through the results, H-JTLBO outperforms all mentioned algorithms in terms of convergence and solution quality.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The used benchmark test functions are available online and derived from Mirjalili’s paper (Gholami et al. 2022).

References

  • Abdullah JM, Ahmed T (2019) Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7:43473–43486

    Article  Google Scholar 

  • Alotaibi SS (2020) Optimization insisted watermarking model: hybrid firefly and Jaya algorithm for video copyright protection. Soft Comput 24(19):14809–14823

    Article  Google Scholar 

  • Aydilek İB (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput J 66:232–249

    Article  Google Scholar 

  • Azizi M, Mousavi Ghasemi SA, Ejlali RG, Talatahari S (2020) Optimum design of fuzzy controller using hybrid ant lion optimizer and Jaya algorithm. Artif Intell Rev 53(3):1553–1584

    Article  Google Scholar 

  • Bansal P, Kumar S, Pasrija S, Singh S (2020) A hybrid grasshopper and new cat swarm optimization algorithm for feature selection and optimization of multi-layer perceptron. Soft Comput 24(20):15463–15489

    Article  Google Scholar 

  • Chegini SN, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems. Appl Soft Comput J 73:697–726

    Article  Google Scholar 

  • Gholami J, Mohammadi S (2018) A novel combination of bees and firefly algorithm to optimize continuous problems. In: 2018 8th international conference on computer and knowledge engineering, ICCKE 2018, pp 40–46

  • Gholami J, Pourpanah F, Wang X (2020) Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput J 93:106402

    Article  Google Scholar 

  • Gholami K, Olfat H, Gholami J (2021a) An intelligent hybrid JAYA and crow search algorithms for optimizing constrained and unconstrained problems. Soft Comput 25(22):14393–14411

    Article  Google Scholar 

  • Gholami J, Mardukhi F, Zawbaa HM (2021b) An improved crow search algorithm for solving numerical optimization functions. Soft Comput 25(14):9441–9454

    Article  Google Scholar 

  • Gholami J, Kamankesh MR, Mohammadi S, Hosseinkhani E, Abdi S (2022) Powerful enhanced Jaya algorithm for efficiently optimizing numerical and engineering problems. Soft Comput 26(11):5315–5333

    Article  Google Scholar 

  • Goudos SK, Yioultsis TV, Boursianis AD, Psannis KE, Siakavara K (2019) Application of new hybrid Jaya grey wolf optimizer to antenna design for 5G communications systems. IEEE Access 7:71061–71071

    Article  Google Scholar 

  • Kaur A, Sharma S, Mishra A (2019) A novel Jaya-BAT algorithm based power consumption minimization in cognitive radio network. Wirel Pers Commun 108(4):2059–2075

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948

  • Kumar V, Yadav SM (2018) Optimization of reservoir operation with a new approach in evolutionary computation using TLBO algorithm and Jaya algorithm. Water Resour Manag 32(13):4375–4391

    Article  Google Scholar 

  • Liu M, Yao X, Li Y (2020) Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Appl Soft Comput J 87:105954

    Article  Google Scholar 

  • Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  • Marinakis Y, Marinaki M, Dounias G (2008) Particle swarm optimization for pap-smear diagnosis. Expert Syst Appl 35(4):1645–1656

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mirjalili S (2016a) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 

  • Mosa MA (2020) A novel hybrid particle swarm optimization and gravitational search algorithm for multi-objective optimization of text mining. Appl Soft Comput J 90:106189

    Article  Google Scholar 

  • Mustafi D, Sahoo G (2019) A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the k-means algorithm with applications in text clustering. Soft Comput 23(15):6361–6378

    Article  Google Scholar 

  • Nenavath H, Jatoth RK (2019) Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl 31(9):5497–5526

    Article  Google Scholar 

  • Pitchaimanickam B, Murugaboopathi G (2020) A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Comput Appl 32(12):7709–7723

    Article  Google Scholar 

  • Qing A (2006) Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems. IEEE Trans Geosci Remote Sens 44(1):116–125

    Article  Google Scholar 

  • Rao RV (2020) Rao algorithms: three metaphor-less simple algorithms for solving optimization problems. Int J Ind Eng Comput 11(1):107–130

    Google Scholar 

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

    Article  Google Scholar 

  • Serrurier M, Prade H (2008) Improving inductive logic programming by using simulated annealing. Inf Sci (NY) 178(6):1423–1441

    Article  MathSciNet  MATH  Google Scholar 

  • Tariq I, AlSattar HA, Zaidan AA, Zaidan BB, Abu Bakar MR, Mohammed RT, Albahri OS, Alsalem MA, Albahri AS (2020) MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput Appl 32(8):3101–3115

    Article  Google Scholar 

  • Venkata Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  • Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408

    Article  Google Scholar 

  • Xiong G, Zhang J, Shi D, Zhu L, Yuan X (2021) Optimal identification of solid oxide fuel cell parameters using a competitive hybrid differential evolution and Jaya algorithm. Int J Hydrogen Energy 46(9):6720–6733

    Article  Google Scholar 

Download references

Funding

The authors declare no funding involved.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Kermanshah Science and Research Branch, Islamic Azad University, Kermanshah, Iran

    Jafar Gholami, Fariba Abbasi Nia & Maryam Sanatifar

  2. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt

    Hossam M. Zawbaa

  3. Applied Science Research Center, Applied Science Private University, Amman, Jordan

    Hossam M. Zawbaa

Authors
  1. Jafar Gholami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fariba Abbasi Nia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maryam Sanatifar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hossam M. Zawbaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by [JG]. Finally, all authors contributed to the writing and revision of the manuscript.

Corresponding author

Correspondence to Hossam M. Zawbaa.

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 the participants of humans or animals.

Informed consent

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gholami, J., Abbasi Nia, F., Sanatifar, M. et al. Effective hybridization of JAYA and teaching–learning-based optimization algorithms for numerical function optimization. Soft Comput 27, 9673–9691 (2023). https://doi.org/10.1007/s00500-023-08201-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-08201-0

Keywords

Search

Navigation