ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number calculations. As an example, we can mention decision-making problems. They defined as optimization Ant Colony Optimization (ACO) is between the best that solves combinatorial The method mimics behavior ants in nature, when look for a food. One algorithm parameters called pheromone, it updated every iteration according quality achieved solutions. intuitionistic fuzzy (propositional) logic was introduced extension Zadeh’s logic. In it, each proposition estimated two values: degree validity non-validity. this paper, propose variants pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) compare results with ACO.