Heuristic particle swarm optimization approach for test point selection with imperfect test

Sen Deng (), Bo Jing and Hongliang Zhou
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Sen Deng: Air Force Engineering University
Bo Jing: Air Force Engineering University
Hongliang Zhou: Air Force Engineering University

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 1, No 4, 37-50

Abstract: Abstract The problem of near-optimal test point set selection with imperfect test is solved by using the heuristic particle swarm optimization (HPSO) algorithm. First, to describe the uncertainty of each test, the testability analysis model and such indexes as fault detection rate, fault isolation rate, and false alarm rate are redefined. A heuristic function is then established to evaluate the detection isolation capability and uncertainty of the test point, which can provide heuristic information to improve the searching efficiency of particle swarm optimization (PSO). The heuristic function and least test cost principle are used as bases to design a fitness function of PSO algorithm for test point selection. Finally, the HPSO algorithm is proposed to select the optimal test point set for two practical systems. Simulation and experiment results show that the method can determine the global optimal test point accurately and effectively while meeting the requirements of testability indexes with least cost.

Keywords: Imperfect test; Test point selection; Particle swarm optimization algorithm; Heuristic function (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-014-0960-1

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