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

中文翻译：

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基于直觉模糊信息素更新的 ACO 应用于多约束背包问题

一些工业和现实生活中的问题很难用传统方法解决，因为它们需要指数数量的计算。例如，我们可以提到决策问题。它们可以被定义为优化问题。蚁群优化 (ACO) 是解决组合优化问题的最佳方法之一。该方法模仿自然界中蚂蚁寻找食物时的行为。算法参数之一称为信息素，它根据所获得解决方案的质量在每次迭代中更新。直觉模糊（命题）逻辑被引入作为 Zadeh 模糊逻辑的扩展。其中，每个命题由两个值估计：有效度和无效度。在本文中，我们提出了两种直觉模糊信息素更新的变体。