A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

  • Sanchari DebEmail author
  • Xiao-Zhi Gao
  • Kari Tammi
  • Karuna Kalita
  • Pinakeswar Mahanta
Methodologies and Application


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.


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



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



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

Compliance with ethical standards

Conflict of interest

The authors declare that they have conflict of interest.

Human and animal rights

We use no animal in this research.


  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 259–264Google Scholar
  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)Google Scholar
  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 55:378–391CrossRefGoogle Scholar
  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):870Google Scholar
  5. Bhattacharjee K, Bhattacharya A, nee Dey SH (2014c) Chemical reaction optimisation for different economic dispatch problems. IET Gener Transm Distrib 8(3):530–541Google Scholar
  6. Bououden S, Chadli M, Karimi HR (2015) An ant colony optimization-based fuzzy predictive control approach for nonlinear processes. Inf Sci 299:143–158MathSciNetzbMATHCrossRefGoogle Scholar
  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):205–214CrossRefGoogle Scholar
  8. Chen YL, He PL, Zhang YH (2015) Combining penalty function with modified chicken swarm optimization for constrained optimization. Adv Intell Syst Res 126:1899–1907Google Scholar
  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. CrossRefGoogle Scholar
  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 48–53Google Scholar
  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 84–89Google Scholar
  12. Deb S, Tammi K, Kalita K, Mahanta P (2018a) Impact of electric vehicle charging station load on distribution network. Energies 11(1):178CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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)Google Scholar
  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 529–553Google Scholar
  16. Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30:1–23Google Scholar
  17. Gao XZ, Govindasamy V, Xu H, Wang X, Zenger K (2015) Harmony search method: theory and applications. Comput Intell Neurosci 2015:39CrossRefGoogle Scholar
  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 685–730Google Scholar
  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):485CrossRefGoogle Scholar
  20. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99CrossRefGoogle Scholar
  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 207–210Google Scholar
  22. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRefGoogle Scholar
  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 118:767–788Google Scholar
  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 3179–3185Google Scholar
  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 2164–2168Google Scholar
  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 86–94Google Scholar
  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):673–687CrossRefGoogle Scholar
  28. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133CrossRefGoogle Scholar
  29. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRefGoogle Scholar
  30. Mirjalili S, Mirjalili SM, Lewis A (2014a) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  31. Mirjalili S, Wang GG, Coelho LDS (2014b) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435CrossRefGoogle Scholar
  32. Munyazikwiye BB, Karimi HR, Robbersmyr KG (2017) Optimization of vehicle-tovehicle frontal crash model based on measured data using genetic algorithm. IEEE Access 5:3131–3138CrossRefGoogle Scholar
  33. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  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):1–30Google Scholar
  35. Rao RV, Kalyankar VD (2011) Parameters optimization of advanced machining processes using TLBO algorithm, vol 20. EPPM, SingaporeGoogle Scholar
  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 395–403Google Scholar
  37. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248zbMATHCrossRefGoogle Scholar
  38. Satapathy SC, Naik A (2014) Modified teaching–learning-based optimization algorithm for global numerical optimization—a comparative study. Swarm Evolut Comput 16:28–37CrossRefGoogle Scholar
  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, 2005Google Scholar
  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 74:1–46CrossRefGoogle Scholar
  41. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15Google Scholar
  42. Wang GG, Tan Y (2017) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 49(2):542–555CrossRefGoogle Scholar
  43. Wang GG, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligence (ISCBI), IEEE, pp 1–5Google Scholar
  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 7:1–23CrossRefGoogle Scholar
  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):394–409CrossRefGoogle Scholar
  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 2206–2211Google Scholar
  47. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  48. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214Google Scholar
  49. Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174CrossRefGoogle Scholar
  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):2345–2356CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sanchari Deb
    • 1
    Email author
  • Xiao-Zhi Gao
    • 2
  • Kari Tammi
    • 3
  • Karuna Kalita
    • 4
  • Pinakeswar Mahanta
    • 4
    • 5
  1. 1.Centre of Energy, Indian Institute of TechnologyGuwahatiIndia
  2. 2.School of ComputingUniversity of Eastern FinlandJoensuuFinland
  3. 3.Department of Mechanical EngineeringAalto UniversityEspooFinland
  4. 4.Department of Mechanical EngineeringIndian Institute of TechnologyGuwahatiIndia
  5. 5.Department of Mechanical EngineeringNational Institute of TechnologyYupiaIndia

Personalised recommendations