Many optimization problems have huge solution spaces, deep restrictions, highly correlated parameters, and operate with uncertain or inconsistent data. Such problems sometimes elude the usual solving methods we are familiar with, forcing us to continuously improve these methods or to even completely reconsider the solving methodologies. When decision makers need faster and better results to more difficult problems, the quality of a decision support system is crucial. To estimate the quality of a decision support system when approaching difficult problems is not easy, but is very important when designing and conducting vital industrial processes or logistic operations. This paper studies the resilience of a solving method, initially designed for the static and deterministic TSP (Traveling Salesman Problem) variant, when applied to an uncertain and dynamic TSP version. This investigation shows how a supplementary level of complexity can be successfully handled. The traditional ant-based system under investigation is infused with a technique which allows the evaluation of its performances when uncertain input data are present. Like the real ant colonies do, the system rapidly adapts to repeated environmental changes. A comparison with the performance of another heuristic optimization method is also done.
|Title of host publication||Combinations of Intelligent Methods and Applications: Proceedings of the 4th International Workshop, CIMA 2014, Limassol, Cyprus, November 2014 (at ICTAI 2014)|
|Editors||Ioannis Hatzilygeroudis, Vasile Palade, Jim Prentzas|
|Place of Publication||Switzerland|
|ISBN (Print)||Print ISBN: 978-3-319-26858-3, Online ISBN: 978-3-319-26860-6|
|Publication status||Published - 28 Jan 2016|
Bibliographical noteThe full text is not available on the repository.
- Combinatorial optimization
- Uncertainty modeling
- Traveling Salesman Problem
- Ant Colony Optimization