The temporal logic of reactive and concurrent systems
The temporal logic of reactive and concurrent systems
Genetic Algorithms for Protocol Validation
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Exploring Very Large State Spaces Using Genetic Algorithms
TACAS '02 Proceedings of the 8th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Ant Colony Optimization
Directed explicit-state model checking in the validation of communication protocols
International Journal on Software Tools for Technology Transfer (STTT)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Information Processing Letters
Searching for liveness property violations in concurrent systems with ACO
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Optimisation of autonomous ship manoeuvres applying Ant Colony Optimisation metaheuristic
Expert Systems with Applications: An International Journal
Using the ACO algorithm for path searches in social networks
Applied Intelligence
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Learning finite-state machines with ant colony optimization
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Ant Colony Optimization (ACO) has been successfully applied to those combinatorial optimization problems which can be translated into a graph exploration. Artificial ants build solutions step by step adding solution components that are represented by graph nodes. The existing ACO algorithms are suitable when the graph is not very large (thousands of nodes) but is not useful when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it. In this paper we study a new ACO model that overcomes the difficulties found when working with a huge construction graph. In addition to the description of the model, we analyze in the experimental section one technique used for dealing with this huge graph exploration. The results of the analysis can help to understand the meaning of the new parameters introduced and to decide which parameterization is more suitable for a given problem. For the experiments we use one real problem with capital importance in Software Engineering: refutation of safety properties in concurrent systems. This way, we foster an innovative research line related to the application of ACO to formal methods in Software Engineering.