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Related Experiment Video

Updated: Sep 22, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

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Equalized Grey Wolf Optimizer with Refraction Opposite Learning.

Lijun Sun1,2,3, Binbin Feng1,2,3, Tianfei Chen1,2,3

  • 1Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.

Computational Intelligence and Neuroscience
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

The novel Equalized Grey Wolf Optimizer (REGWO) enhances swarm intelligence algorithms by incorporating refraction opposition-based learning and an equilibrium pool. REGWO effectively overcomes local optima and improves population diversity for better optimization performance.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Grey Wolf Optimizer (GWO) is a nature-inspired metaheuristic algorithm simulating wolf pack hunting behaviors.
  • Traditional GWO suffers from premature convergence and stagnation in local optima, limiting its effectiveness in complex optimization tasks.
  • Maintaining population diversity is crucial for global search capabilities in swarm intelligence algorithms.

Purpose of the Study:

  • To propose an enhanced Grey Wolf Optimizer, termed Equalized Grey Wolf Optimizer (REGWO), to address the limitations of the traditional GWO.
  • To improve the exploration and exploitation balance within the GWO framework.
  • To enhance the algorithm's ability to escape local optima and maintain population diversity.

Main Methods:

  • Introduced refraction opposition-based learning to increase population variety, especially in later iterations.
  • Implemented an equilibrium pool strategy to prevent premature convergence and reduce the likelihood of falling into local extrema.
  • Evaluated REGWO on 21 standard benchmark functions and the IEEE CEC 2019 test suite.

Main Results:

  • REGWO demonstrated superior performance compared to traditional GWO and other competing algorithms across most benchmark functions.
  • The proposed enhancements effectively improved population diversity and mitigated the tendency for local optima entrapment.
  • REGWO achieved competitive or superior results on complex optimization problems presented in the IEEE CEC 2019 test suite.

Conclusions:

  • The Equalized Grey Wolf Optimizer (REGWO) presents a significant improvement over the standard GWO, offering enhanced global search capabilities.
  • REGWO's novel components, refraction opposition-based learning and equilibrium pool, effectively address GWO's convergence issues.
  • The algorithm shows strong potential for application in various real-world optimization problems requiring robust and efficient search strategies.