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A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

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

Abstract

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.

Keywords

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

Abbreviations

GA

Genetic Algorithm

SA

Simulated Annealing

GSA

Gravitational Search Algorithm

PSO

Particle Swarm Optimization

CS

Cuckoo Search

EHO

Elephant Herding Optimization

EWA

Earthworm Optimization Algorithm

GWO

Grey Wolf Optimization

WOA

Whale Optimization Algorithm

ABC

Artificial Bee Colony

BSA

Bird Swarm Algorithm

CSO

Chicken Swarm Optimization

ICSO

Improved chicken Swarm Optimization

DE

Differential Evolution

BA

Bat Algorithm

IRRO

Improved Raven Roosting Optimization

NFL

No Free Lunch

TLBO

Teaching–Learning-based Optimization

mTLBO

Modified Teaching–Learning-based Optimization

ICSOTLBO

Improved Chicken Swarm Optimization Teaching–Learning-based Optimization

SaDE

Self-Adaptive Differential Evolution

jDE

New Self-Adaptive Differential Evolution

EPSDE

Differential Evolution with ensemble of parameter

APSO

Adaptive Particle Swarm Optimization

OLPSO

Orthogonal Particle Swarm Optimization

CLPSO

Comprehensive Learning Particle Swarm Optimization

CMA-ES

Covariance Matrix Adaptation Evolution Strategy

SPC-PNX

Real Parameter Genetic Algorithm

BPSOGSA

Binary Particle Swarm Optimization Gravitational Search Algorithm

BGSA

Binary Gravitational Search Algorithm

SD

Standard Deviation

EV

Electric Vehicle

RCCRO

Real-Coded Chemical Reaction Optimization

HSA

Harmony Search Algorithm

Notes

Acknowledgements

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.

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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

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