A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

  • 103 Accesses

  • 1 Citations


Chicken Swarm Optimization (CSO) is a novel swarm intelligence-based algorithm known for its good performance on many benchmark functions as well as real-world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint-handling mechanism. Further, the work also proposes synergy of the improved version of CSO with Teaching–Learning-based Optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state-of-the-art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9



Genetic Algorithm


Simulated Annealing


Gravitational Search Algorithm


Particle Swarm Optimization


Cuckoo Search


Elephant Herding Optimization


Earthworm Optimization Algorithm


Grey Wolf Optimization


Whale Optimization Algorithm


Artificial Bee Colony


Bird Swarm Algorithm


Chicken Swarm Optimization


Improved chicken Swarm Optimization


Differential Evolution


Bat Algorithm


Improved Raven Roosting Optimization


No Free Lunch


Teaching–Learning-based Optimization


Modified Teaching–Learning-based Optimization


Improved Chicken Swarm Optimization Teaching–Learning-based Optimization


Self-Adaptive Differential Evolution


New Self-Adaptive Differential Evolution


Differential Evolution with ensemble of parameter


Adaptive Particle Swarm Optimization


Orthogonal Particle Swarm Optimization


Comprehensive Learning Particle Swarm Optimization


Covariance Matrix Adaptation Evolution Strategy


Real Parameter Genetic Algorithm


Binary Particle Swarm Optimization Gravitational Search Algorithm


Binary Gravitational Search Algorithm


Standard Deviation


Electric Vehicle


Real-Coded Chemical Reaction Optimization


Harmony Search Algorithm


  1. Ahmed K, Hassanien AE, Bhattacharyya S (2017) A novel chaotic chicken swarm optimization algorithm for feature selection. In: 2017 third international conference on research in computational intelligence and communication networks (ICRCICN), IEEE, pp 259–264

  2. Ballester PJ, Stephenson J, Carter JN, Gallagher K (2005) Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. In: The 2005 IEEE congress on evolutionary computation, 2005, IEEE (vol 1, pp 498–505)

  3. Bhattacharjee K, Bhattacharya A, nee Dey SH (2014a) Oppositional real coded chemical reaction optimization for different economic dispatch problems. Int J Electr Power Energy Syst 55:378–391

  4. Bhattacharjee K, Bhattacharya A, Dey SHN (2014b) Teaching-learning-based optimization for different economic dispatch problems. Sci Iran Trans D Comput Sci Eng Electr 21(3):870

  5. Bhattacharjee K, Bhattacharya A, nee Dey SH (2014c) Chemical reaction optimisation for different economic dispatch problems. IET Gener Transm Distrib 8(3):530–541

  6. Bououden S, Chadli M, Karimi HR (2015) An ant colony optimization-based fuzzy predictive control approach for nonlinear processes. Inf Sci 299:143–158

  7. Cai X, Gao XZ, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-Inspired Comput 8(4):205–214

  8. Chen YL, He PL, Zhang YH (2015) Combining penalty function with modified chicken swarm optimization for constrained optimization. Adv Intell Syst Res 126:1899–1907

  9. Chen S, Yang RR, Yang R et al (2016) A parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimization. Discret Dyn Nat Soc 2016:3795961.

  10. Deb S, Ghosh D, Mohanta DK (2016) Optimal configuration of stand-alone hybrid microgrid considering cost, reliability and environmental factors. In: 2016 international conference on signal processing, communication, power and embedded system (SCOPES), IEEE, pp 48–53

  11. Deb S, Kalita K, Gao XZ, TammiK, Mahanta P (2017) Optimal placement of charging stations using CSO-TLBO algorithm. In: 2017 third international conference on research in computational intelligence and communication networks (ICRCICN), IEEE, pp 84–89

  12. Deb S, Tammi K, Kalita K, Mahanta P (2018a) Impact of electric vehicle charging station load on distribution network. Energies 11(1):178

  13. Deb S, Tammi K, Kalita K, Mahanta P (2018b) Review of recent trends in charging infrastructure planning for electric vehicles. WIREs Energy Environ 2018:e306.

  14. Deb S, Gao XZ, Tammi K, Kalita K, Mahanta P (2019a) Recent studies on chicken swarm optimization algorithm: a review (2014–2018). Artif Intell Rev 1–29 (in press)

  15. Deb S, Kalita K, Mahanta P (2019b) Distribution network planning considering the impact of electric vehicle charging station load. In: Smart power distribution systems. Academic Press, pp 529–553

  16. Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30:1–23

  17. Gao XZ, Govindasamy V, Xu H, Wang X, Zenger K (2015) Harmony search method: theory and applications. Comput Intell Neurosci 2015:39

  18. Ghosh D, Deb S, Mohanta DK (2017) Reliability evaluation and enhancement of microgrid incorporating the effect of distributed generation. In: Handbook of distributed generation. Springer, Cham, pp 685–730

  19. Goodarzi H, Kazemi M (2017) A novel optimal control method for islanded microgrids based on droop control using the ICA-GA algorithm. Energies 10(4):485

  20. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99

  21. Han M, Liu S (2017) An improved binary chicken swarm optimization algorithm for solving 0-1 knapsack problem. In: 2017 13th international conference on computational intelligence and security (CIS), IEEE, pp 207–210

  22. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

  23. Kumar DS, Veni S (2018) Enhanced energy steady clustering usingconvergence node based path optimizationwith hybrid chicken swarm algorithm inMANET. Int J Pure Appl Math 118:767–788

  24. Li YF, Zhan ZH, Lin Y, ZhangJ (2015) Comparisons study of APSO OLPSO and CLPSO on CEC2005 and CEC2014 test suits. In: 2015 IEEE congress on evolutionary computation (CEC), IEEE, pp 3179–3185

  25. Liang S, Feng T, SunG, Zhang J, Zhang H (2016) Transmission power optimization for reducing sidelobe via bat-chicken swarm optimization in distributed collaborative beamforming. In: 2016 2nd IEEE international conference on computer and communications (ICCC), IEEE, pp 2164–2168

  26. Meng XB, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence, Springer, Cham, pp 86–94

  27. Meng XB, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird Swarm Algorithm. J Exp Theor Artif Intell 28(4):673–687

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

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

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

  31. Mirjalili S, Wang GG, Coelho LDS (2014b) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435

  32. Munyazikwiye BB, Karimi HR, Robbersmyr KG (2017) Optimization of vehicle-tovehicle frontal crash model based on measured data using genetic algorithm. IEEE Access 5:3131–3138

  33. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

  34. Rao R (2016) Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett 5(1):1–30

  35. Rao RV, Kalyankar VD (2011) Parameters optimization of advanced machining processes using TLBO algorithm, vol 20. EPPM, Singapore

  36. Rao RV, Waghmare GG (2013) Solving composite test functions using teaching-learning-based optimization algorithm. In: Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA), Springer, Berlin, Heidelberg, pp 395–403

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

  38. Satapathy SC, Naik A (2014) Modified teaching–learning-based optimization algorithm for global numerical optimization—a comparative study. Swarm Evolut Comput 16:28–37

  39. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005, 2005

  40. Torabi S, Safi-Esfahani F (2018) A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing. J Supercomput 74:1–46

  41. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15

  42. Wang GG, Tan Y (2017) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 49(2):542–555

  43. Wang GG, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligence (ISCBI), IEEE, pp 1–5

  44. Wang GG, Deb S, Coelho LDS (2015b) Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int J Bio-Inspired Comput 7:1–23

  45. Wang GG, Deb S, Gao XZ, Coelho LDS (2016) A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-Inspired Comput 8(6):394–409

  46. Wang K, Li Z, Cheng H, Zhang K (2017) Mutation chicken swarm optimization based on nonlinear inertia weight. In: 2017 3rd IEEE international conference on computer and communications (ICCC), IEEE, pp 2206–2211

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

  48. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214

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

  50. Zhai Z, Li S, Liu Y, Li Z (2015) Teaching-learning-based optimization with a fuzzy grouping learning strategy for global numerical optimization. J Intell Fuzzy Syst 29(6):2345–2356

Download references


Xiao-Zhi Gao’s research work was partially supported by the National Natural Science Foundation of China (NSFC) under Grant 51875113.

Author information

Correspondence to Sanchari Deb.

Ethics declarations

Conflict of interest

The authors declare that they have conflict of interest.

Human and animal rights

We use no animal in this research.

Additional information

Publisher's Note

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

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Deb, S., Gao, X., Tammi, K. et al. A New Teaching–Learning-based Chicken Swarm Optimization Algorithm. Soft Comput (2019).

Download citation


  • Algorithm
  • Benchmark
  • Chicken Swarm Optimization
  • Function
  • Hybrid
  • Teaching–Learning-based Optimization