An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems
作者： Madjid Khichane , Patrick Albert , Christine Solnon
DOI: 10.1007/978-3-642-11169-3_9
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摘要: We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both use ACO to adapt parameters: pheromone trails are associated with parameter values; these represent the learnt desirability using values and used set in a probabilistic way. The differ granularity learning. experimentally evaluate on algorithm solving constraint satisfaction problems.
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