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加权中心人工蜂群算法
引用本文:孙辉,谢海华,赵嘉,邓志诚.加权中心人工蜂群算法[J].控制与决策,2019,34(10):2115-2124.
作者姓名:孙辉  谢海华  赵嘉  邓志诚
作者单位:南昌工程学院信息工程学院,南昌330000;鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室,南昌330000;江西省水信息协同感知与智能处理重点实验室,南昌330000;南昌工程学院信息工程学院,南昌,330000
基金项目:国家自然科学基金项目(61261039, 51669014);江西省教育厅落地计划项目(KJLD13096).
摘    要:针对人工蜂群算法收敛速度慢、局部搜索能力差等缺点,提出一种新的改进人工蜂群算法.新算法依据蜜源适应值进行排序,将排序结果作为权值,构造一个虚拟蜜源,即加权中心.若加权中心优于当前最优解,则取代当前最优解,以便得到更好的当前最优解.在加权中心的基础上,增加全维搜索策略,以改善算法的局部搜索能力.两种策略的应用能够加快算法的收敛速度,增强局部搜索能力.在经典的22个基准测试函数上,对新算法的有效性进行实验仿真分析,实验结果表明,所提出算法在求解精度和速度上均有显著提高,在给定等同的时间下远高于其他算法.

关 键 词:人工蜂群算法  加权中心  虚拟蜜源  收敛速度  当前最优解  局部搜索

Artificial bee algorithm with weighted center
SUN Hui,XIE Hai-hu,ZHAO Jia and DENG Zhi-cheng.Artificial bee algorithm with weighted center[J].Control and Decision,2019,34(10):2115-2124.
Authors:SUN Hui  XIE Hai-hu  ZHAO Jia and DENG Zhi-cheng
Affiliation:School of Information Engineering,Nanchang Institute of Technology,Nanchang 330000,China;National-Local Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area,Nanchang 330000,China;Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang 330000,China,School of Information Engineering,Nanchang Institute of Technology,Nanchang 330000,China,School of Information Engineering,Nanchang Institute of Technology,Nanchang 330000,China;National-Local Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area,Nanchang 330000,China;Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang 330000,China and School of Information Engineering,Nanchang Institute of Technology,Nanchang 330000,China
Abstract:In view of the disadvantages of slow convergence speed and weak local search ability of the artificial bee algorithm, this paper proposes a new improved artificial bee algorithm. The honey source is sorted according to the fitness value, and then the sorting result is used as the weight value to construct a virtual honey source, namely the weighted center. If the weighted center is superior to the current optimal solution, the current optimal solution is replaced to obtain a better current optimal solution. On the basis of the weighted center, the full-dimension search strategy is added to improve the local search capability of the algorithm. The application of two strategies speeds up the convergence speed of the algorithm and enhances the local search capability. The validity of the new algorithm on the classical 22 benchmark test function is analyzed. The experimental results show that the proposed algorithm can significantly improve the accuracy and speed. Under a given equivalent time, the solving accuracy and the convergence speed of the proposed algorithm are much higher than that the comparison algorithm.
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