Speed Regulation of Scale Adjustment in Quantum-Inspired Optimization Algorithm

MU Lei; WANG Peng

doi:10.12263/DZXB.20201305

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Speed Regulation of Scale Adjustment in Quantum-Inspired Optimization Algorithm

PAPERS | 更新时间：2025-12-08

- Speed Regulation of Scale Adjustment in Quantum-Inspired Optimization Algorithm
- ACTA ELECTRONICA SINICA Vol. 50, Issue 7, Pages: 1664-1673(2022)
- 作者机构：
  
  1.西南民族大学计算机科学与工程学院，四川成都 610041
  2.电子科技大学计算机科学与工程学院，四川成都 611731
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201305
  CLC： TP301.6;
- Received：18 November 2020，
  
  Revised：2021-03-01，
  
  Published：25 July 2022
- 稿件说明：
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穆磊,王鹏.量子启发式优化算法的尺度动态调速机制[J].电子学报,2022,50(07):1664-1673.

MU Lei,WANG Peng.Speed Regulation of Scale Adjustment in Quantum-Inspired Optimization Algorithm[J].ACTA ELECTRONICA SINICA,2022,50(07):1664-1673.
穆磊,王鹏.量子启发式优化算法的尺度动态调速机制[J].电子学报,2022,50(07):1664-1673. DOI： 10.12263/DZXB.20201305.

MU Lei,WANG Peng.Speed Regulation of Scale Adjustment in Quantum-Inspired Optimization Algorithm[J].ACTA ELECTRONICA SINICA,2022,50(07):1664-1673. DOI： 10.12263/DZXB.20201305.

摘要

尺度在量子启发谐振子优化算法中起着重要作用，反映了解空间中搜索探针的分辨率.当前研究中以固定速度调整尺度并未合理利用尺度资源.此外，候选解可能会因高斯采样的聚集效应而陷入边界.本文提出了一种在一定程度上反映了适应度利用效率的指标，称为适应度进化利用率.在此基础上，本文提出了一种具有尺度动态调速机制和边界映射反弹策略的量子启发式优化方法.该算法通过与尺度调整因子相关的适应度进化利用率动态调节尺度调整速度，通过2种不同的边界映射反弹策略增加可行解的多样性.将本算法与多种流行优化算法在基准测试函数集上进行对比实验，采用了一种带有动态可接受误差的成功率评估机制保证公平性，实验结果表明该算法具有较强的竞争性.

Abstract

The scale plays an important role in the multiscale quantum-inspired harmonic oscillator algorithm

and it reflects the resolution of searching probes in solution space. The fixed-rate speed of scale adjustment in the current researches leads to unreasonable use of the resources on a specific scale. Besides

candidate solutions may be trapped in the boundary for the aggregation effect of Gaussian sampling. A metric called fitness evolution ratio

which reflects the fitness utilization efficiency to a certain extent

is proposed in this paper. On this basis

a quantum-inspired optimization algorithm is put forward with speed regulation of scale adjustment and boundary mapping rebounding strategy. The algorithm dynamically adjusts the scale adjustment speed through the fitness evolution ratio related to the scale adjustment factor. Besides

it implements two different boundary mapping rebounding strategies to increase the diversity of candidate solutions. Comparison experiments are conducted on the benchmark functions with a variety of compared algorithms. For a fair comparison

an evaluation mechanism of success rate with dynamic acceptable errors is utilized. The results show considerable competitiveness for our scheme.

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references

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