Grey wolf optimization applied to the maximum flow problem

Abstract

The problem of getting the maximum flow from source to destination in networks is investigated in this paper. A proposed algorithm is presented in order to solve Maximum Flow problem by using Grey Wolf Optimization (GWO). The GWO is a recently established meta-heuristics for optimization, inspired by grey wolves (Canis Lupus). In addition; in this current research, K-means clustering algorithm is used to group each 12 vertices with each other at one cluster according to GWO constraint. This work is implemented and tested various datasets between 50 vertices and 1000 vertices. The simulation results show rapprochement between experimental and theoretical results.