A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

Sanchari Deb*, Xiao Zhi Gao, Kari Tammi, Karuna Kalita, Pinakeswar Mahanta

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

30 Citations (Scopus)
167 Downloads (Pure)


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.

Original languageEnglish
Number of pages19
JournalSoft Computing
Early online date19 Aug 2019
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed


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


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