一种基于模拟退火-改进二进制粒子群算法的测试优化选择方法

张钊旭; 王志杰; 李建辰; 王贵奇; 许 军; 杨进候

doi:10.11993/j.issn.2096-3920.2017.02.003

一种基于模拟退火-改进二进制粒子群算法的测试优化选择方法

doi: 10.11993/j.issn.2096-3920.2017.02.003

1.
中国船舶重工集团公司第705研究所, 陕西西安, 710077
2.
水下信息与控制国家重点实验室, 陕西西安, 710077

详细信息

作者简介:
张钊旭(1993-), 男, 在读硕士, 主要研究方向为鱼雷总体技术.

中图分类号: TJ6; TN06; TP301.6
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- 文章访问数: 826
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- 被引次数: 0
出版历程
- 收稿日期: 2017-03-06
- 修回日期: 2017-04-06
- 刊出日期: 2017-06-20

An Optimal Test Selection Method Based on Simulated Annealing-Improved Binary Particle Swarm Optimization Algorithm

1.
The 705 Research Institute, China Shipbuilding Industry Corporation, Xi′an 710077, China
2.
Science and Technology on Underwater Information and Control Laboratory, Xi′an 710077, China

摘要

摘要: 武器系统装备性能的不断提高, 复杂度的不断增加对测试性设计提出更高要求。为了解决测试性设计中测试优化选择这一非确定性多项式难题(NP-hard), 文中提出一种模拟退火-改进二进制粒子群算法(SA-IBPSO)用于求解最优完备测试集。该算法以二进制粒子群算法(BPSO)为基础框架, 采用异步变化的学习因子, 产生时变的压缩因子, 以增强BPSO算法的全局搜索能力, 确保其收敛性, 并取消了对速度的边界限制; 然后, 与具有概率突跳能力的模拟退火算法(SA)相结合, 以避免BPSO算法在求解过程中陷入局部最优。最后, 通过案例验证, 并与其他算法的运行结果进行比较, 证明该算法可以更有效地解决测试优化选择问题。
- 测试性设计 /
- 测试优化选择 /
- 模拟退火 /
- 二进制粒子群算法
Abstract: To solve the non-deterministic polynomial hard(NP-hard) problem of test selection in the design for testability of weapon system, an optimal test selection method based on simulated annealing-improved binary particle swarm optimization(SA-IBPSO) algorithm is proposed to acquire the best complete test set. This algorithm is on the basis of binary particle swarm optimization(BPSO), and uses asynchronous dynamic learning divisors to obtain time-varying contraction factor, which facilitates the global searching speed, guarantees the convergence of BPSO, and abrogates the boundary constraint of particle velocity in BPSO. And the simulated annealing algorithm with probabilistic jumping ability is combined to prevent BPSO from converging to local optimum. Simulation test shows that compared with other algorithms, the proposed algorithm is more effective in acquiring global optimal solution to optimal test selection.
- design for testability /
- optimal test selection /
- simulated annealing /
- binary particle swarm optimization

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参考文献(11)

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