Abstract
This paper studies an improved artificial bee colony algorithm, and two problems have been solved when the artificial colony algorithm is applied to objective optimization: the problem of slow convergence and premature aging problem. When the improved artificial bee colony algorithm is applied to land resources optimization problems, studies show the following two points. First, compared with the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, artificial bee colony algorithm has better adaptability and robustness in solving multivariate and multi peak global optimization problems. Second, compared with artificial bee colony algorithm, the improved artificial bee colony algorithm converges faster, the overall fitness increases by 8.9%, the maximum error is no more than 1%, and the short and medium term optimization has a high precision.
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