A short convergence proof for a class of ant colony optimization algorithms

By Thomas Stützle, Marco Dorigo. IEEE Transactions on Evolutionary Computation, 2002

415 citations recorded by the source corpus.

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Detection of multiple source locations using a glowworm metaphor with applications to collective robotics (2005, result): “This proof is similar to Stützle and Dorigos' [9] proposition proving the bounded nature of pheromone levels in their ant-colony algorithm.”
Ant colony optimization: Introduction and recent trends (2005, background): “Dorigo and Stutzle, first in [ 90 ] and later in [36] ,p resented ap roof for the convergence in value, as well as ap roof for the convergence in solution, for algorithms from this class.”
A novel decentralized platform for peer-to-peer energy trading market with blockchain technology (2021, methodology): “The ACO method is a highly efficient algorithm with the ability to achieve optimal solutions given suitable parameters and design schemes [55, 56, 57].”
Evolutionary optimization as applied to inverse scattering problems (2009, methodology): “On the same line of reasoning of [72], Stutzle and Dorigo proved in [54, 152] that for a class of ACO-based algorithms with a lower bound ψmin on the pheromone level, the success expectancy of the optimization is equal to Pr { opt k = best } 1 − η, with η being small as desired and close to zero for”
Ant colony optimization theory: A survey (2005, background): “assignment problems [14,47,46,63,66], scheduling problems [11,17,27, 51 ,64], and vehicle routing problems [29,59].”