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Issue:Intuitionistic fuzzy estimation of the ant colony optimization starting points: Part 2

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Title of paper: Intuitionistic fuzzy estimation of the ant colony optimization starting points: Part 2
Author(s):
Stefka Fidanova
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 25A, 1113 Sofia, Bulgaria
stefka@parallel.bas.bg
Pencho Marinov
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 25A, 1113 Sofia, Bulgaria
pencho@parallel.bas.bg
Presented at: 15th ICIFS, Burgas, 11-12 May 2011
Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 17 (2011) Number 2, pages 75—81
Download:  PDF (136  Kb, File info)
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 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).
Keywords: Ant colony optimization, Intuitionistic fuzzy sets, Knapsack problem.
AMS Classification: 03E72, 90C59, 68T20
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