Skip to main content
SpringerLink
Log in

Dung Beetle-Inspired Local Search in PSO for LSSMTWTS Problem

  • Conference paper
  • First Online:
Computer Vision and Robotics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The large-scale single machine total weighted tardiness scheduling problem (LSSMTWTSP) is a complex NP-Hard problem. Instead, one should set a set of unrelated tasks on a machine with different criteria. The goal of the problem is to find the minimum possible weighted retardation. For the past few decades, the particle swarm optimization algorithm (PSOA) has shown commendable performance in optimization. Researchers are creating many new forms of PSO to solve complex problems. This paper covers an impressive local search technique inspired by Dung beetle orientation and foraging activities in PSOs. The strategy designed is named the Dung beetle-inspired PSO (DBPSO) algorithm. The efficiency and accuracy of the employed DBPSO strategy have experimented on the LSSMTWTS problem, which shows that DBPSO can be considered an efficient method for determining combinatorial optimization dilemmas.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Acharyulu BVS, Gorripotu TS, Azar AT, Mohanty B, Pilla R, Kumar S, Serrano FE, Kamal NA (2021) Automatic generation control of multi-area multi-source deregulated power system using moth flame optimization algorithm. In: Communication and intelligent systems. Springer, pp 717–729

    Google Scholar 

  2. Bansal JC, Sharma H, Jadon SS (2014) Spider monkey optimization algorithm for numerical optimization. Memetic Comput 6(1):31–47

    Article  Google Scholar 

  3. Chugh A, Sharma VK, Kumar S, Nayyar A, Qureshi B, Bhatia MK, Jain C (2021) Spider monkey crow optimization algorithm with deep learning for sentiment classification and information retrieval. IEEE Access 9:24249–24262

    Article  Google Scholar 

  4. Ding J, Lü Z, Cheng TCE, Xu L (2017) A hybrid evolutionary approach for the single-machine total weighted tardiness problem. Comput Ind Eng 108:70–80

    Article  Google Scholar 

  5. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley

    Google Scholar 

  6. Goyal M, Goyal D, Kumar S (2020) Dragon-AODV: efficient ad hoc on-demand distance vector routing protocol using dragon fly algorithm. In: Soft computing: theories and applications. Springer, Singapore, pp 181–191

    Google Scholar 

  7. Goyal M, Kumar S, Sharma VK, Goyal D (2020) Modified Dragon-AODV for efficient secure routing. In: Advances in computing and intelligent systems. Springer, pp 539–546

    Google Scholar 

  8. Gupta S, Kumari R, Singh RP (2021) Lunar cycle inspired PSO for single machine total weighted tardiness scheduling problem. In: Evolutionary intelligence, pp 1–12

    Google Scholar 

  9. Jain S, Sharma VK, Kumar S (2020) Robot path planning using differential evolution. In: Advances in computing and intelligent systems. Springer, pp 531–537

    Google Scholar 

  10. Jouglet A, Baptiste P, Carlier J (2002) Exact procedures for single machine total cost scheduling. In: 2002 IEEE international conference on systems, man and cybernetics, vol 6. IEEE, p 4

    Google Scholar 

  11. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report. TR06. Erciyes University Press, Erciyes

    Google Scholar 

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

    Google Scholar 

  13. Kumar S, Sharma B, Sharma VK, Poonia RC (2021) Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evol Intell 14(2):293–304

    Article  Google Scholar 

  14. Kumar S, Sharma B, Sharma VK, Sharma H, Bansal JC (2020) Plant leaf disease identification using exponential spider monkey optimization. Sustain Comput Inf Syst 28:100283

    Google Scholar 

  15. Lenstra JK, Kan AHGR, Brucker P (1977) Complexity of machine scheduling problems. Ann Discr Math 1:343–362

    Article  MathSciNet  Google Scholar 

  16. Nayyar A, Nguyen NG, Kumari R, Kumar S (2020) Robot path planning using modified artificial bee colony algorithm. In: Frontiers in intelligent computing: theory and applications. Springer, pp 25–36

    Google Scholar 

  17. Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res (IJSIR) 1(1):1–16

    Article  MathSciNet  Google Scholar 

  18. Schrage L, Baker KR (1978) Dynamic programming solution of sequencing problems with precedence constraints. Oper Res 26(3):444–449

    Article  Google Scholar 

  19. Sharma A, Chaturvedi R, Dwivedi U, Kumar S (2021) Multi-level image segmentation of color images using opposition based improved firefly algorithm. Rec Adv Comput Sci Commun For Rec Pat Comput Sci 14(2):521–539

    Article  Google Scholar 

  20. Sharma A, Sharma H, Bhargava A, Sharma N, Fibonacci series based local search in spider monkey optimisation for transmission expansion planning. Int J Swarm Intell (in press)

    Google Scholar 

  21. Sharma Nirmala, Sharma Harish, Sharma Ajay (2018) Beer froth artificial bee colony algorithm for job-shop scheduling problem. Appl Soft Comput 68:507–524

    Article  Google Scholar 

  22. Sharma N, Sharma H, Sharma A (2020) An effective solution for large scale single machine total weighted tardiness problem using lunar cycle inspired artificial bee colony algorithm. IEEE/ACM Trans Comput Biol Bioinform 17(5):1573–1581. https://doi.org/10.1109/TCBB.2019.2897302

  23. Sharma N, Sharma H, Sharma A, Bansal JC (2020) Dung beetle inspired local search in artificial bee colony algorithm for unconstrained and constrained numerical optimisation. Int J Intell Eng Inf 8(4):268–304

    Google Scholar 

  24. Sharma P, Sharma H, Kumar S, Sharma K (2019) Black-hole gbest differential evolution algorithm for solving robot path planning problem. In: Harmony search and nature inspired optimization algorithms. Springer, pp 1009–1022

    Google Scholar 

  25. Shekhawat SS, Sharma H, Kumar S (2021) Memetic spider monkey optimization for spam review detection problem. Big data ahead of print. http://doi.org/10.1089/big.2020.0188

  26. Shekhawat SS, Sharma H, Kumar S, Nayyar A, Qureshi B (2021) BSSA: binary salp swarm algorithm with hybrid data transformation for feature selection. IEEE Access 9:14867–14882

    Google Scholar 

  27. Storn R, Price K (1997) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. J Glob Opt 11:341–359

    Article  Google Scholar 

  28. Tanaka Shunji, Fujikuma Shuji, Araki Mituhiko (2009) An exact algorithm for single-machine scheduling without machine idle time. J Sched 12(6):575–593

    Article  MathSciNet  Google Scholar 

  29. Tasgetiren MF, Sevkli M, Liang Y-C, Gencyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Proceedings of the IEEE congress on evolutionary computation. CEC 2004, vol 2. IEEE, pp 1412–1419

    Google Scholar 

  30. Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Career Point University, Kota, India

    Shruti Gupta

  2. CHRIST (Deemed to be University), Bengaluru, India

    Rajani Kumari

Authors
  1. Shruti Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajani Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  3. Babu Banarasi Das University, Lucknow, Uttar Pradesh, India

    Praveen Kumar Shukla

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, S., Kumari, R. (2022). Dung Beetle-Inspired Local Search in PSO for LSSMTWTS Problem. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_41

Download citation

Publish with us

Policies and ethics

Search

Navigation