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人工蜂群(ABC)算法存在着收敛速度不够快、易陷入局部最优的缺陷.针对这一问题,提出一种改进的人工蜂群(DCABC)算法.应用反学习的初始化方法产生初始解,引入分治策略对蜜源进行优化,在采蜜蜂发布更新的蜜源信息后,跟随蜂选择最优蜜源,并采用分治策略进行迭代优化.通过对经典测试函数的反复实验及与其他算法的比较,表明了所提出的算法具有良好的加速收敛效果,提高了全局搜索能力与效率.
The artificial bee colony (ABC) algorithm has some drawbacks such as fast convergence speed and easily falling into the local optimum.To solve this problem, an improved artificial bee colony (DCABC) algorithm is proposed.An initial learning solution is used to generate the initial solution, After introducing the updated nectar information, the honeybee selected the optimal nectar source and optimized the nectar by using the divide and conquer strategy.Through repeated experiments on classical test functions and comparison with other algorithms, It shows that the proposed algorithm has a good acceleration convergence effect and improves the global search ability and efficiency.