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A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

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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.

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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

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Acknowledgements

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

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Authors and Affiliations

  1. Centre of Energy, Indian Institute of Technology, Guwahati, India

    Sanchari Deb

  2. School of Computing, University of Eastern Finland, Joensuu, Finland

    Xiao-Zhi Gao

  3. Department of Mechanical Engineering, Aalto University, Espoo, Finland

    Kari Tammi

  4. Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India

    Karuna Kalita & Pinakeswar Mahanta

  5. Department of Mechanical Engineering, National Institute of Technology, Yupia, Arunachal Pradesh, India

    Pinakeswar Mahanta

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  1. Sanchari Deb
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  2. Xiao-Zhi Gao
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Correspondence to Sanchari Deb.

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Deb, S., Gao, XZ., Tammi, K. et al. A New Teaching–Learning-based Chicken Swarm Optimization Algorithm. Soft Comput 24, 5313–5331 (2020). https://doi.org/10.1007/s00500-019-04280-0

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