Learning Network Flow Based on Rough Set Flow Graphs and ACO Clustering in Distributed Cognitive Environments

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.