Skip to main content

Advertisement

Springer Nature Link
Log in

Binary whale optimization algorithm and its application to unit commitment problem

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Whale optimization algorithm is a novel metaheuristic algorithm that imitates the social behavior of humpback whales. In this algorithm, the bubble-net hunting strategy of humpback whales is exploited. However, this algorithm, in its present form, is appropriate for continuous problems. To make it applicable to discrete problems, a binary version of this algorithm is being proposed in this paper. In the proposed approach, the solutions are binarized and sigmoidal transfer function is utilized to update the position of whales. The performance of the proposed algorithm is evaluated on 29 benchmark functions. Furthermore, unpaired t test is carried out to illustrate its statistical significance. The experimental results depict that the proposed algorithm outperforms others in respect of benchmark test functions. The proposed approach is applied on electrical engineering problem, a real-life application, named as “unit commitment”. The proposed approach uses the priority list to handle spinning reserve constraints and search mechanism to handle minimum up/down time constraints. It is tested on standard IEEE systems consisting of 4, 10, 20, 40, 80, and 100 units and on IEEE 118-bus system and Taiwan 38-bus system as well. Experimental results reveal that the proposed approach is superior to other algorithms in terms of lower production cost.

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
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Goldberg D (1989) Genetic algorithms in search, optimization, and machine. Addison-Wesley, Boston

    MATH  Google Scholar 

  2. Kennedy N, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, pp 1942–1948

  3. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–35

    Article  Google Scholar 

  4. Yang X-S, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  5. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28:459–471

    Article  Google Scholar 

  9. Srinivasa KG, Venugopal KR, Patnaik LM (2007) A self-adaptive migration model genetic algorithm for data mining applications. Inf Sci 177(20):4295–4313

    Article  MATH  Google Scholar 

  10. Wu T-H, Chang C-C, Chung S-H (2008) A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst Appl 34(3):1609–1617

    Article  Google Scholar 

  11. Yuan X, Nie H, Su A, Wang L, Yuan Y (2009) An improved binary particle swarm optimization for unit commitment problem. Expert Syst Appl 36(4):8049–8055

    Article  Google Scholar 

  12. Amiri M, Khanohamadi S (2013) A primary unit commitment approach with a modification process. Appl Soft Comput 13:1007–1015

    Article  Google Scholar 

  13. Panwar LK, Reddy SK, Verma A, Panigrahi BK, Kumar R (2018) Binary grey wolf optimizer for large scale unit commitment problem. Swarm Evol Comput 38:251–266

    Article  Google Scholar 

  14. Senjyu T, Shimabukuro K, Uezato K, Funabashi T (2003) A fast technique for unit commitment problem by extended priority list. IEEE Trans Power Syst 18(2):882–888

    Article  Google Scholar 

  15. Ongsakul W, Petcharaks N (2004) Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Trans Power Syst 19:620–628

    Article  Google Scholar 

  16. Venkatesh B, Jamtsho T, Gooi HB (2007) Unit commitment—a fuzzy mixed integer linear programming solution. IET Gener Transm Distrib 1(5):836–846

    Article  Google Scholar 

  17. Cohen AI, Yoshimura M (1983) A Branch-and-Bound algorithm for unit commitment. IEEE Trans Power Appar Syst 102(2):444–451

    Article  Google Scholar 

  18. Su C-C, Hsu Y-Y (1991) Fuzzy dynamic programming: an application to unit commitment. IEEE Trans Power Syst 6(3):1231–1237

    Article  Google Scholar 

  19. Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11(1):83–92

    Article  Google Scholar 

  20. Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 21(1):68–76

    Article  Google Scholar 

  21. Jeong YW, Park JB, Jang SH, Lee KY (2009) A new quantum inspired binary PSO for thermal unit commitment problems. In: Proceedings of IEEE international conference on intelligent system applications to power systems, Curitiba, Brazil, pp 1–6

  22. Juste KA, Kita H, Tanaka E, Hasegawa J (1999) An evolutionary programming solution to the unit commitment problem. IEEE Trans Power Syst 14(4):1452–1459

    Article  Google Scholar 

  23. Hadji MM, Vahidi B (2012) A solution to the unit commitment problem using imperialistic competition algorithm. IEEE Trans Power Syst 27(1):117–124

    Article  Google Scholar 

  24. Balci H, Valenzuela J (2004) Scheduling electric power generations using particle swarm optimization combined with the Lagrangian relaxation method. Int J Appl Math Comput Sci 14(3):411–421

    MathSciNet  MATH  Google Scholar 

  25. Saber NA, Salimi M, Mirabbbasi D (2016) A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm. Energy 117(1):272–280

    Article  Google Scholar 

  26. Kamboj VK, Bath SK, Dhillon JS (2016) Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem. Int J Electr Power Energy Syst 77:228–249

    Article  Google Scholar 

  27. Wang B, Li Y, Watada J (2011) Re-scheduling the unit commitment problem in fuzzy environment. In: Proceedings of IEEE international conference on fuzzy systems, Taipei, Taiwan, pp 1090–1095

  28. Kamboj VK (2016) A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput Appl 27(6):1643–1655

    Article  Google Scholar 

  29. Mallipeddi R, Suganthan PN (2014) Unit commitment—a survey and comparison of conventional and nature inspired algorithms. Int J BioInspir Comput 6(2):71–90

    Article  Google Scholar 

  30. Rashedi E, Nezamabadi-pour H, Saryazdi S (2010) BSGA: binary gravitational search algorithm. Nat Comput 9(3):727–745

    Article  MathSciNet  MATH  Google Scholar 

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

  32. Eid HF (2017) Binary whale optimization: an effective swarm algorithm for feature selection. Int J Metaheuristics 7(1):67–79

    Article  MathSciNet  Google Scholar 

  33. Digalakis JG, Margaritis KG (2002) An experimental study of benchmarking functions for genetic algorithms. Int J Comput Math 79(4):403–416

    Article  MathSciNet  MATH  Google Scholar 

  34. Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings of IEEE swarm intelligence symposium, Pasadena, CA, pp 68–75

  35. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–681

    Article  Google Scholar 

  36. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE international conference on computational cybernetics and simulation, Orlando, pp 4104–4108

  37. Mirjalili S, Mirjalili SM, Yang X-S (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681

    Article  Google Scholar 

  38. Mirjalili S (2016) 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  Google Scholar 

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

    Article  Google Scholar 

  40. Jeong Y-W, Lee W-N, Kim H-H, Park J-B, Shin J-R (2009) Thermal unit commitment using binary differential evolution. J Electr Eng Technol 4(3):323–329

    Article  Google Scholar 

  41. Sriyanyong P, Song YH (2005) Unit commitment using particle swarm optimization combined with Lagrange relaxation. In: IEEE power engineering society general meeting, San Francisco, USA, pp 1–8

  42. Panwar LK, Reddy SK, Kumar R (2015) Binary fireworks algorithm based thermal unit commitment. Int J Swarm Intell Res 6(2):87–101

    Article  Google Scholar 

  43. Singhal PK, Naresh R, Sharma V (2015) A novel strategy-based hybrid binary artificial bee colony algorithm for unit commitment problem. Arab J Sci Eng 40(5):1455–1469

    Article  Google Scholar 

  44. Khanmohammadi S, Amiri M, TarafdarHaque M (2010) A new three-stage method for solving unit commitment problem. Energy 35(7):3072–3080

    Article  Google Scholar 

  45. Roque LAC (2016) Optimization methods for the unit commitment problem in electric power systems. Dissertation, University of Porto

  46. Bai X, Wei H (2009) Semi-definite programming-based method for security-constrained unit commitment with operational and optimal power flow constraints. IET Gener Transm Distrib 3(2):182–197

    Article  Google Scholar 

  47. Huang KY, Yang HT, Huang CL (1998) A new thermal unit commitment approach using constraint logic programming. IEEE Trans Power Syst 13(3):936–945

    Article  Google Scholar 

  48. Chandrasekaran K, Hemamalini S, Simon SP, Padhy NP (2009) Thermal unit commitment using binary/real coded artificial bee colony algorithm. Electr Power Syst Res 84:109–119

    Article  Google Scholar 

  49. Chandrasekaran K, Simon SP (2013) Optimal deviation based firefly algorithm tuned fuzzy design for multi-objective UCP. IEEE Trans Power Syst 28:460–471

    Article  Google Scholar 

  50. Koodalsamy B, Veerayan MB, Koodalsamy C, Simon SP (2016) Firefly algorithm with multiple workers for the power system unit commitment. Turk J Electr Eng Comput Sci 24:4773–4789

    Article  Google Scholar 

  51. Sun L, Zhang Y, Jiang C (2006) A matrix real-coded genetic algorithm to the unit commitment problem. Elect Power Syst Res 76(9–10):716–728

    Article  Google Scholar 

  52. Saber AY, Senjyu T, Yona A, Funabashi T (2007) Unit commitment computation by fuzzy adaptive particle swarm optimization. IET Gener Transm Distrib 1:456–465

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Vijay Kumar

  2. Electronics and Communication Engineering Department, Delhi Technological University, Delhi, India

    Dinesh Kumar

Authors
  1. Vijay Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dinesh Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vijay Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Appendix 1

Appendix 1

See Fig. 4 and Tables 22, 23, 24, 25, 26, 27, 28, 29.

Table 22 Unimodal benchmark test functions
Full size table
Table 23 Multimodal benchmark test functions
Full size table
Table 24 Multimodal benchmark test functions with fixed dimension
Full size table
Table 25 Composite benchmark test functions
Full size table
Table 26 Load demand for 4-power unit system (MW)
Full size table
Table 27 Test data for 4-power unit system
Full size table
Table 28 Load demand for 10-power unit system (MW)
Full size table
Table 29 Test data for 10-power unit system
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, V., Kumar, D. Binary whale optimization algorithm and its application to unit commitment problem. Neural Comput & Applic 32, 2095–2123 (2020). https://doi.org/10.1007/s00521-018-3796-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3796-3

Keywords