Intuitionistic Fuzzy Estimation of the Ant Colony Optimization Starting Points

The ability of ant colonies to form paths for carrying food is rather fascinating. The problem is solved collectively by the whole colony. This ability is explained by the fact that ants communicate in an indirect way by laying trails of pheromone. The higher the pheromone trail within a particular direction, the higher the probability of choosing this direction. The collective problem solving mechanism has given rise to a metaheuristic referred to es Ant Colony Optimization (ACO). On this work we use intoitionistic fuzzy estimation of start nodes with respect to the quality of the solution. Various start strategies are offered. Sensitivity analysis of the algorithm behavior according estimation parameters is made. As a test problem is used Multidimensional (Multiple) Knapsack Problem (MKP).