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Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment.

Xiaomeng Zhao1, Lin Liu1

  • 1School of Economics and Management, Shihezi University, Xinjiang Shihezi 832000, China.

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|August 22, 2022
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Summary
This summary is machine-generated.

This study introduces a Particle Swarm Optimization (PSO) algorithm for analyzing income gaps within edge computing environments. The findings reveal a lagging adjustment in stock income gaps, indicating a persistent upward trend favoring high-income groups.

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Area of Science:

  • Economics
  • Computer Science
  • Computational Intelligence

Background:

  • Income inequality is a persistent societal challenge.
  • Edge computing environments offer new possibilities for data analysis.
  • Traditional methods for income gap analysis have limitations.

Purpose of the Study:

  • To propose a novel method for exploring income gaps using the Particle Swarm Optimization (PSO) algorithm.
  • To apply PSO within an edge computing environment for enhanced analysis.
  • To investigate the dynamics of stock and traffic income gaps.

Main Methods:

  • Utilizing the Particle Swarm Optimization (PSO) algorithm, inspired by bird flocking behavior.
  • Implementing quantile grouping for innovative Gini coefficient calculation.
  • Simulating biological group models and quantifying abstract problems.

Main Results:

  • The Gini coefficient for traffic income significantly decreased in 2017-2018.
  • Stock income Gini coefficient adjustment speed was found to be lagging.
  • Post-1986, a facilitation effect increased the stock income gap, favoring high-income groups.

Conclusions:

  • The PSO algorithm effectively identifies income gaps in edge computing settings.
  • The study highlights a growing income flow towards high-income groups.
  • Policy suggestions are provided based on the identified income gap trends.