Fifteenth Int. Conf. on IFSs, Burgas, 11-12 May 2011 NIFS 17 (2011), 2, 75-81 Intuitionistic fuzzy estimation of the ant colony optimization starting points: Part 2 Stefka Fidanova and Pencho Marinov Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev Str., Bl. 25A, 1113 Sofia, Bulgaria E-mails: {stefka,pencho}@parallel.bas.bg Abstract: 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 Optimiza- tion (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). Keywords: Ant colony optimization, Intuitionistic fuzzy sets, Knapsack problem. AMS Classification: 03E72, 90C59, 68T20 1 Introduction A large number of real-life optimization problems in science, engineering, economics, and busi- ness are complex and difficult to solve. They can not be solved in an exact manner within a reasonable amount of computational resources. Using approximate algorithms is the main alter- native to solve this class of problems. The approximate algorithms are specific heuristics, which are problem dependent, and metaheuristics, which are more general approximate algorithms ap- plicable to a large variety of optimization problems. One of the most successful metaheuristic is Ant Colony Optimization (ACO) [4]. ACO algorithms have been inspired by the real ants’ behavior. In nature, ants usually wander randomly, and upon finding food return to their nest while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but follow the trail, returning and reinforcing it if they eventually find food. However, as time passes, the pheromone starts to evaporate. The more time it takes for an ant to travel down the path and back again, the more time the pheromone has to evaporate and the path to become less prominent.In comparison, a shorter path will be visited by more ants and thus the pheromone density remains high for a longer time. 75