Paper Info
Title
Learning network flow based on rough set flow graphs and ACO clustering in distributed cognitive environments.
Abstract
This paper presents the use of a modified collective behavior strategy of ant colonies to find approximate sets in the multi-objective optimization problem. The currently used methods search for non-dominated solutions, which takes place directly on the basis of definitions in the previously generated finite set of admissible ratings, searching in the space of goals by analyzing active constraints, solving optimization tasks in terms of all subsequent individual optimization criteria and adopting optimization criteria in order to form a substitute criterion of optimization in the form of a combination of linear criteria with appropriately selected weighting factors. However, these methods are ineffective in many cases. Therefore, the authors of the article propose a new approach based on the use of rough sets flow graphs to control the strategy of communicating artificial ants in distributed cognitive environments. The use of this approach allows to maximize the number of generated solutions and finding non-dominated solutions for the multiple objectives.
Year
Venue
Keywords
2018
SE4COG@ICSE
Cognitive systems services, Network Flow algorithms, Rough Sets flow graphs, ACO clustering
DocType
ISBN
Citations 
Conference
978-1-4503-5740-1
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Arkadiusz Lewicki1578.61
Eugeniusz Eberbach2388.70