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On the Finite-Time Dynamics of Ant Colony Optimization
Authors:Walter J Gutjahr
Institution:(1) Department of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria
Abstract:An analytical framework for investigating the finite-time dynamics of ant colony optimization (ACO) under a fitness-proportional pheromone update rule on arbitrary construction graphs is developed. A limit theorem on the approximation of the stochastic ACO process by a deterministic process is demonstrated, and a system of ordinary differential equations governing the process dynamics is identified. As an example for the application of the presented theory, the behavior of ACO on three different construction graphs for subset selection problems is analyzed and compared for some basic test functions. The theory enables first rough theoretical predictions of the convergence speed of ACO. AMS 2000 Subject classification 68W20, 68W40
Keywords:Ant colony optimization  Convergence speed  Probabilistic algorithms  Stochastic approximation  Stochastic optimization
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