Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm\r\nwas proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the\r\nalgorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize\r\nthe diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar\r\nposition; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the\r\nsearch stage of algorithm; the parallel search of dual population considerably improved the convergence rate.Through simulation\r\nexperiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm\r\nhad faster convergence rate and the capacity of jumping out of local optimum faster.
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