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An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy.

Yu-Xian Zhang1, Xiao-Yi Qian2, Hui-Deng Peng1

  • 1School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China.

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Summary
This summary is machine-generated.

This study introduces a novel allele real-coded quantum evolutionary algorithm. The enhanced algorithm improves convergence speed and prevents premature convergence in optimization problems.

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Area of Science:

  • Artificial Intelligence
  • Computational Optimization
  • Quantum Computing

Background:

  • Quantum evolutionary algorithms (QEAs) face challenges with convergence rate and premature convergence.
  • Real-coded genetic representations are effective for continuous optimization problems.

Purpose of the Study:

  • To present an allele real-coded quantum evolutionary algorithm (ARQEA) with a hybrid updating strategy.
  • To enhance convergence rate and prevent premature convergence in optimization tasks.

Main Methods:

  • Real variables are encoded using probability superposition of alleles.
  • A hybrid updating strategy balances global and local search, defining superior alleles.
  • A guided evolutionary process and variable scale contraction update alleles.
  • The H ε gate is incorporated to mitigate premature convergence.
  • Global convergence is proven using Markov chain analysis.

Main Results:

  • The proposed ARQEA demonstrates superior performance compared to traditional genetic algorithms, QEAs, and double chains QEAs.
  • Experimental results confirm significant improvements in convergence rate.
  • Enhanced search accuracy was observed in continuous optimization problems.

Conclusions:

  • The allele real-coded quantum evolutionary algorithm with a hybrid updating strategy effectively improves convergence rate and search accuracy.
  • The algorithm successfully addresses premature convergence issues in quantum evolutionary computation.
  • This approach offers a promising advancement for solving complex continuous optimization problems.