TestAnt: An ant colony system approach to sequential testing under precedence constraints

Authors:
BüLent ÇAtay;ÖZgüR ÖZlüK;Tonguç íNlüYurt
Affiliations:
Sabanci University, Faculty of Engineering and Natural Sciences, Tuzla 34956, Istanbul, Turkey;San Francisco State University, College of Business, 94132 CA, USA;Sabanci University, Faculty of Engineering and Natural Sciences, Tuzla 34956, Istanbul, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 5
Cited 2

Optimal sequential inspections of reliability systems subject to parallel-chain precedence constraints

Discrete Applied Mathematics - Special issue on the satisfiability problem and Boolean functions
A cost-reducing question-selection algorithm for propositional knowledge-based systems

Annals of Mathematics and Artificial Intelligence
Finding optimal satisficing strategies for and-or trees

Artificial Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
On a multimode test sequencing problem

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Sequential testing policies for complex systems under precedence constraints

Expert Systems with Applications: An International Journal
Optimal ordering of independent tests with precedence constraints

Discrete Applied Mathematics

Quantified Score

Hi-index	12.05

Visualization

Abstract

We consider the problem of minimum cost sequential testing (diagnosis) of a series (or parallel) system under precedence constraints. We model the problem as a nonlinear integer program. We develop and implement an ant colony algorithm for the problem. We demonstrate the performance of this algorithm for special type of instances for which the optimal solutions can be found in polynomial time. In addition, we compare the performance of the ant colony algorithm with a branch and bound algorithm for randomly generated general instances of the problem. The ant colony algorithm is particularly effective as the problem size gets larger.