量子衍生蜂群算法的设计与实现

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (7): 1480-1489.

量子衍生蜂群算法的设计与实现

杨淑云¹, 李盼池²

1.东北石油大学招生就业处,大庆 163318;
2.东北石油大学计算机与信息技术学院, 大庆 163318

收稿日期:2014-07-11 修回日期:2014-08-09 出版日期:2015-07-08 发布日期:2020-07-31
作者简介:杨淑云(1968-), 女, 黑龙江安达人, 讲师. 研究方向为神经网络和智能优化算法; 李盼池(1969-), 男, 河北大城人, 教授, 博士, 研究方向为量子神经网络和量子衍生优化算法。
基金资助:
国家自然科学基金(61170132); 黑龙江省自然科学基金(F2015021)

Design and Implementation of Quantum-Inspired Bee Colony Algorithm

Yang Shuyun¹, Li Panchi²

1. Admission and Employment office, Northeast Petroleum University, Daqing 163318, China;
2. School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China

Received:2014-07-11 Revised:2014-08-09 Online:2015-07-08 Published:2020-07-31

摘要/Abstract

摘要： 为提高人工蜂群算法的优化能力,提出一种量子衍生蜂群算法。在该算法中,蜂群采用基于Bloch球面描述的量子比特编码;采用量子比特在Bloch球面上的绕转旋转实现进化搜索;采用泡利矩阵获得量子比特的Bloch坐标;通过解空间变换获得优化问题的实际解。该方法的突出优点是能够同时调整量子比特的两个参数,并自动实现两个调整量的最佳匹配。函数极值优化及水淹层识别的实验结果表明,该方法的优化能力比普通蜂群算法确有明显提高。

关键词: 量子计算, 蜂群优化, Bloch球面旋转, 算法设计

Abstract: To enhance the performance of artificial bee colony algorithm, a quantum-inspired bee colony algorithm was proposed. In the proposed approach, the bees were encoded with the qubits described on the Bloch sphere. The evolutionary search was achieved by rotating the qubit about the rotation axis on the Bloch sphere. The Bloch coordinates of qubit can be obtained by measuring with the Pauli matrices, and the optimization solutions can be presented through the solution space transformation. The highlight advantages of this method are the ability to simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities. The experimental results show that the proposed method obviously outperforms the classical one for some benchmark functions and the water flooded layer identification.

Key words: quantum computing, bee colony optimizing, Bloch sphere rotating, algorithm designing

中图分类号:

TP183

杨淑云, 李盼池. 量子衍生蜂群算法的设计与实现[J]. 系统仿真学报, 2015, 27(7): 1480-1489.

Yang Shuyun, Li Panchi. Design and Implementation of Quantum-Inspired Bee Colony Algorithm[J]. Journal of System Simulation, 2015, 27(7): 1480-1489.

参考文献

[1] Karaboga D.An Idea Based On Honey Bee Swarm For Numerical Optimization [R]. Technical Report- TR06, Engineering Faculty, Computer Engineering Depart- ment, Erciyes University, Kayseri, Turkey, 2005.
[2] Bahriye A, Dervis K.A modified Artificial Bee Colony algorithm for real-parameter optimization[J]. Information Sciences(S0020-0255), 2012, 192(6): 120-142.
[3] Xiang W L, An M Q.An efficient and robust artificial bee colony algorithm for numerical optimization[J]. Computers & Operations Research(S0305-0548), 2013, 40(5): 1256-1265.
[4] Li G Q, Niu P F, Xiao X J.Development and investigation of efficient artificial bee colony algorithm for numerical function optimization[J]. Applied Soft Computing(S1568-4946), 2012, 12(1): 320-332.
[5] Karaboga D, Akay B, Ozturk C.Artificial bee colony (ABC) optimization algori- thm for training feed-forward neural networks[C]. Proceedings of the 4th International Conference on Computer Science, Kitakyushu, Japan, August 16-18, 2007:318-329.
[6] Karaboga N.A new design method based on artificial bee colony algorithm for digital IIR filter[J]. Journal of the Franklin Institute(S0016-0032), 2009, 346(4): 328-348.
[7] Rao R, Narasimham S, Ramalingaraju M.Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm[J]. International Journal of Electrical Power and Energy Systems Engineering(S0142-0615), 2008, 1(2): 709-715.
[8] Singh A.An artificial bee colony algorithm for the leaf-constrained mini- mum spanning tree problem[J]. Applied Soft Computing(S1568-4946), 2009, 9(2): 625-631.
[9] 丁海军, 李峰磊. 蜂群算法在TSP问题上的应用及参数改进[J]. 中国科技信息, 2008, 25(3): 241-243.
[10] 康飞, 李俊杰, 许青. 改进人工蜂群算法及其在反演分析中的应用[J]. 水电能源科学, 2009, 27(1): 126-129.
[11] Duan H B, Xu C F, Xing Z H.A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems[J]. International Journal of Neural Systems(S0129-0657), 2010, 20(1): 39-50.
[12] 李盼池. 基于量子位Bloch坐标的量子遗传算法及其应用[J]. 控制理论与应用, 2008, 25(6): 985-989.
[13] 李盼池, 林晶晶. 基于Bloch球面搜索的混沌量子免疫算法[J]. 系统工程与电子技术, 2012, 34(12): 2592-2598.
[14] 李盼池, 王琪超, 施光尧. 基于Bloch球面搜索的量子粒子群优化算法[J]. 计算物理, 2013, 30(3): 454-462.
[15] Karaboga D, Akay B, Ozturk C.Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks[C]//Proceedings of the 4th International Conference on Modeling Decisions for Artificial Intelligence, Springer, August 16-18, 2007: 318-329.
[16] 李盼池, 王海英, 宋考平, 等. 量子势阱粒子群优化算法的改进研究[J]. 物理学报, 2012, 61(6): 060302.