Optimal Selection of Imperfect Tests Based on Improved Quantum-Inspired Evolutionary Algorithm

LEI Hua-jun; QIN Kai-yu

doi:10.3969/j.issn.0372-2112.2017.10.022

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Optimal Selection of Imperfect Tests Based on Improved Quantum-Inspired Evolutionary Algorithm

更新时间：2025-07-16

- Optimal Selection of Imperfect Tests Based on Improved Quantum-Inspired Evolutionary Algorithm
- Acta Electronica Sinica Vol. 45, Issue 10, Pages: 2464-2472(2017)
- 作者机构：
  
  1. 电子科技大学航空航天学院,四川,成都,611731
  2. 西南科技大学信息工程学院,四川,绵阳,621010
- 作者简介：
- 基金信息：
  
  Doctoral Fund of Southwest University of Science and Technology (No.15zx7155)
- DOI：10.3969/j.issn.0372-2112.2017.10.022
  CLC： TP206
- Published：2017
- 稿件说明：
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LEI Hua-jun, QIN Kai-yu. Optimal Selection of Imperfect Tests Based on Improved Quantum-Inspired Evolutionary Algorithm[J]. Acta Electronica Sinica, 2017, 45(10): 2464-2472.
DOI：

LEI Hua-jun, QIN Kai-yu. Optimal Selection of Imperfect Tests Based on Improved Quantum-Inspired Evolutionary Algorithm[J]. Acta Electronica Sinica, 2017, 45(10): 2464-2472. DOI： 10.3969/j.issn.0372-2112.2017.10.022.

摘要

测试优化选择是复杂电子系统测试性设计中的一个重要问题.首先从测试容差的角度分析了测试发生漏检和虚警的原因，在此基础上建立了测试不可靠条件下一种新的测试选择模型，模型以测试代价、漏检代价和虚警代价之和最小为优化目标，以故障检测率和故障隔离率为约束条件；然后提出一种改进的量子进化算法对模型求解，该算法通过改进一种已有可靠测试选择算法而成，包括种群初始化、适应度计算和种群的进化策略.最后通过两个仿真实例验证了求解算法及模型的有效性和优越性.

Abstract

Optimal selection of tests is an important problem that arises in design for testability for complex electronic systems.Firstly

from the perspective of test tolerance

the reason of how tests produce miss detection and false alarm is analyzed.Then

a new mathematic model for the problem of test selection in the presence of imperfect tests is developed.It consists of minimizing the sum of test cost

miss detection cost and false alarm cost

subject to lower bound constraints on fault detection rate and fault isolation rate.To optimize the model

an improved quantum-inspired evolutionary algorithm is proposed.It is formed by making some improvements to an extant algorithm that has been used for optimal selection of perfect tests

including population initialization

fitness calculation and the strategy of population evolution.Finally

two simulation examples are used to validate the effectiveness and superiority of the solution method and the model.

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