Arithmetic and parent-centric headless chicken crossover operators for dynamic particle swarm optimization algorithms



This paper conducts an analysis of various strategies for incorporating the headless chicken macromutation operator into a dynamic particle swarm optimization algorithm. Seven variations of the dynamic headless chicken guaranteed convergence particle swarm optimization algorithm are proposed and evaluated on a diverse set of single-objective dynamic benchmark problems. Competitive performance was demonstrated by the headless chicken PSO algorithms when compared to, amongst others, a quantum particle swarm optimization algorithm.


Headless chicken macromutation operator Particle swarm optimization Dynamic optimization Quantum particle swarm optimization 



This work is based on the research supported by the National Research Foundation (NRF) of South Africa (Grant Number 46712). The opinions, findings and conclusions or recommendations expressed in this article are that of the authors alone, and not that of the NRF. The NRF accepts no liability whatsoever in this regard.

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Conflict of interest

No other potential conflicts of interest exist, and the article does not contain any studies with human or animal participants. Sub Elements in Acknowledgement


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Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringUniversity of PretoriaPretoriaSouth Africa
  2. 2.Department of Computer ScienceUniversity of PretoriaPretoriaSouth Africa

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