| 量子衍生布谷鸟搜索算法 |
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| 引用本文： | 李盼池,杨淑云,刘显德,潘俊辉,肖红,曹茂俊.量子衍生布谷鸟搜索算法[J].计算机系统应用,2017,26(9):122-127. |
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| 作者姓名： | 李盼池 杨淑云 刘显德 潘俊辉 肖红 曹茂俊 |
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| 作者单位： | 东北石油大学 计算机与信息技术学院, 大庆 163318,东北石油大学 计算机与信息技术学院, 大庆 163318,东北石油大学 计算机与信息技术学院, 大庆 163318,东北石油大学 计算机与信息技术学院, 大庆 163318,东北石油大学 计算机与信息技术学院, 大庆 163318,东北石油大学 计算机与信息技术学院, 大庆 163318 |
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| 基金项目： | 黑龙江省教育厅科学技术研究项目（12541059） |
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| 摘 要： | 为提高布谷鸟搜索算法的寻优能力，通过在经典布谷鸟搜索算法中引入量子计算机制，提出了一种量子衍生布谷鸟搜索算法.该算法采用量子比特编码个体，采用泡利矩阵确定旋转轴，采用Levy飞行原理确定旋转角度，采用量子比特在Bloch球面上的绕轴旋转实现个体更新.标准函数极值优化的实验结果表明，与传统布谷鸟搜索算法相比，该算法的搜索能力确有明显提升.
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| 关 键 词： | 仿生智能优化 群智能优化 布谷鸟算法 量子衍生优化 算法设计 |
| 收稿时间： | 2016/12/19 0:00:00 |
Quantum-Inspired Cuckoo Search Algorithm |
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| LI Pan-Chi,YANG Shu-Yun,LIU Xian-De,PAN Jun-Hui,XIAO Hong and CAO Mao-Jun.Quantum-Inspired Cuckoo Search Algorithm[J].Computer Systems& Applications,2017,26(9):122-127. |
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| Authors: | LI Pan-Chi YANG Shu-Yun LIU Xian-De PAN Jun-Hui XIAO Hong and CAO Mao-Jun |
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| Affiliation: | School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China,School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China,School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China,School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China,School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China and School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China |
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| Abstract: | In order to improve the search ability of the cuckoo search algorithm, this paper proposes a quantum-inspired cuckoo search algorithm by introducing the quantum computing mechanism into the classical cuckoo search algorithm.. In the proposed algorithm, the qubits are used to encode individuals, and the Pauli matrixes are employed to determine rotation axis. The Levy flight principle is applied to obtain rotation angle, and the rotation of the qubits on the Bloch sphere is used to update the individuals. The experimental results of extreme optimization of benchmark test functions show that the proposed algorithm is obviously superior to the classical cuckoo search algorithm in optimization ability. |
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| Keywords: | bionic intelligent optimization swarm intelligence optimization cuckoo algorithm quantum-inspired optimization algorithm design |
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