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 as Ant Colony Optimization. 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 to estimation parameters is made. As a test problem Multidimensional (Multiple) Knapsack Problem is used.
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Fidanova, S., Atanassov, K., Marinov, P. (2012). Intuitionistic Fuzzy Estimation of the Ant Colony Optimization Starting Points. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2011. Lecture Notes in Computer Science, vol 7116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29843-1_25
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DOI: https://doi.org/10.1007/978-3-642-29843-1_25
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