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控制理论与应用 2010
Ant-colony-genetic algorithm with adaptive parameters based on grey prediction and normal cloud
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Abstract:
Ant colony algorithm with positive feedback has a good capability of global convergence; while the genetic algorithm(GA) is with a fast performance in global search. A hybrid algorithm with adaptive parameters is proposed to take advantages of the above two optimization algorithm. Using the grey prediction, we obtain in the ant colony strategy the estimates of the maximum (minimum) trail limits which are controlled for avoiding the immature convergence. Meanwhile, we employ the cloud models to build a set of association rules which are used to adaptively adjust algorithm parameters by information feedback during the iterative process, thus reducing the reliance on initial parameters. Simulation results for job-shop scheduling problem(JSP) and traveling salesman problem(TSP) validate the algorithm.