基于融合算法的测试优化选择问题研究

计算机科学 ›› 2013, Vol. 40 ›› Issue (Z6): 54-57.

基于融合算法的测试优化选择问题研究

刘刚,黎放,狄鹏

海军工程大学管理工程系武汉430033;海军工程大学管理工程系武汉430033;海军工程大学管理工程系武汉430033

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家部委基金项目(4314231428),海军工程大学自然科学基金项目(HGDQNJJ12041)资助

Research on Optimal Test Selection Based on Fused Algorithm

LIU Gang,LI Fang and DI Peng

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 测试优化选择是个集覆盖问题,而启发式算法是求解集覆盖问题的有效方法。文中将遗传算法、BP神经网络和模拟退火算法进行融合,提出了一种融合算法,该算法充分利用遗传算法全局搜索能力强、BP神经网络训练能力强和模拟退火算法搜索速度快的优点,既避免陷入局部最优的现象,又提高了搜索的效率和精度。该算法已应用于求解测试优化问题。实例证明,该算法能够快速有效地求得测试优化问题的最优解。

关键词: 测试选择,遗传算法,BP神经网络,模拟退火算法

Abstract: Test optimization selection is a set cover problem,and heuristic algorithm for set covering problem is effective method.A genetic simulated annealing neural network fused algorithm was proposed by fusing the genetic algorithm,BP neural network and the simulated annealing algorithm,the genetic algorithm global search ability,strong ability of BP neural network training algorithm and fast search ability of simulated annealing algorithm were made full use of in this algorithm,the phenomenon falling into local optimum was avoided,and also the search efficiency and accuracy wad improved,the algorithm is applied to solve the test optimization selection problem.Example proves,this algorithm can effectively and quickly obtain test the optimal solution of optimization problems.

Key words: Test selection,Genetic algorithm,BP neural network,Simulated annealing algorithm

刘刚,黎放,狄鹏. 基于融合算法的测试优化选择问题研究[J]. 计算机科学, 2013, 40(Z6): 54-57. https://doi.org/

LIU Gang,LI Fang and DI Peng. Research on Optimal Test Selection Based on Fused Algorithm[J]. Computer Science, 2013, 40(Z6): 54-57. https://doi.org/

参考文献

[1] 刘刚,黎放,胡斌．基于相关性模型的舰船装备测试性分析与建模 [J]．海军工程大学学报,2012,24(8):46-51
[2] 石君友．测试性设计分析与验证[M]．北京:国防工业出版社,2011
[3] 黄艳新,周春光,邹淑雪,等．一种求解类覆盖问题的混合算法[J]．软件学报,2005,16(4):513-522
[4] Yuan X,Cohen M B,Memon A M.Covering array sampling of input event sequences for automated GUI testing[C]∥22nd International Conference on Automated Software Engineering．2007:405-408
[5] Sampath S,Bryce R C,Viswanath G,et al．Prioritizing user-session-based test cases for application testing[C]∥1st International Conference on Software Testing,Verification,and Validation．2008:141-150
[6] 苏永定,钱彦岭,邱静．基于启发式搜索策略的测试选择问题研究[J]．中国测试技术,2005,31(5):46-49
[7] 张延生,黄考利,连光耀．基于改进AHP法的导弹装备测试性参数选择方法研究[J]．计算机测量与控制,2011,19(2):412-414
[8] 吴涛,叶晓慧,王红霞．基于量子遗传算法测试选择问题的研究[J]．计算机测量与控制,2010,18(11):2508-2510
[9] Amonchanchaigul T,Kreesuradej W．Input selection using binary particle swarm optimization[C]∥International Conference on Computational Intelligence for Modeling Control and Automation,and International Conference on Intelligent Agents,Web Technologies and Internet Conference(CIMCA-IAWTIC’06)．2006
[10] Sadri J,Suen C Y．A genetic binary particle swarm optimization[C]∥2006IEEE Congress on Evolutionary Computation Shera-ton Vancouver Wall Centre Hotel,Vancouver,BC．Canada,July 2006:656-663
[11] Afshinmanesh F,Marandi A,Rahimi-Klan A．A novel binaryparticle swarm optimization method using system[C]∥EUROCON 2005．Serbia,November 2005:217-220
[12] 陈希祥,邱静,刘冠军．基于混合二进制粒子群-遗传算法的测试优化选择研究[J]．仪器仪表学报,2009,30(8):1674-1683
[13] 蒯伟．测试点的优化选择[J]．电子测试,2012(2):91-94
[14] Cao P B,Xiao R B．Assembly planning using a novel immune approach[J].International Journal of Advanced Manufacturing Technology,2007,1(7): 770-778
[15] 李鸣,高娜,姜为学．测试选择和诊断策略设计的列表寻优法[J]．电光与控制,2010,17(12):71-74
[16] Kuhn R,Lei Y,Kacker R．Practical combinatorial testing:beyondpairwise[J].IT Professional,2008,10(3):19-23
[17] Golonek T,Rutkowski J．Genetic-algorithm-based method foroptimal analog test points selection [J]．IEEE Transactions on Circuits and Systems-II:Express Briefs,2007,54(2):117-121
[18] Starzyk J A,Liu D,Liu Z H,et al．Entropy-based optimum test points selection for analog fault dictionary techniques[J]．IEEE Transactions on Instrumentation and Measurement,2004,53(3):754-761

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed