基于逻辑运算的离散人工蜂群算法

doi:10.3969/j.issn.0372-2112.2015.11.004

电子学报 ›› 2015, Vol. 43 ›› Issue (11): 2161-2166.DOI: 10.3969/j.issn.0372-2112.2015.11.004

基于逻辑运算的离散人工蜂群算法

朱冰莲¹, 朱方方¹, 苏红宏², 石锐¹, 段青言¹, 李晶^1,3

1. 重庆大学通信工程学院, 重庆 400044;
2. 重庆大学航天航空学院, 重庆 400044;
3. 酒泉卫星发射中心, 甘肃酒泉 732750

收稿日期:2014-06-19 修回日期:2015-04-27 出版日期:2015-11-25
- 作者简介:
- 朱冰莲 女,1959出生于四川富顺,重庆大学教授,主要研究方向为信号与信息处理、模式识别、图像处理等.E-mail:zhubl@cqu.edu.cn;朱方方 男,1989年12月生于湖北省天门市,现为重庆大学硕士研究生.主要研究方向为智能优化算法及在认知无线电中的应用.
- 基金资助:
- 国家自然科学基金 (No.61201177)

Discrete Artificial Bee Colony Algorithm Based on Logic Operation

ZHU Bing-lian¹, ZHU Fang-fang¹, SU Hong-hong², SHI Rui¹, DUAN Qing-yan¹, LI Jing^1,3

1. Department of Communication Engineering, Chongqing University, Chongqing 400044, China;
2. Department of Aerospace Engineering, Chongqing University, Chongqing 400044, China;
3. Jiuquan Satellite Launch Center, Jiuquan, Gansu 732750, China

Received:2014-06-19 Revised:2015-04-27 Online:2015-11-25 Published:2015-11-25

摘要/Abstract

摘要：

针对当前离散人工蜂群算法冗余度高、探索性能差、容易陷入早熟等问题,提出一种基于逻辑运算的离散人工蜂群算法.通过引入一系列的逻辑运算,一方面解决了当前离散人工蜂群算法中存在的解不更新问题,提高了算法的搜索效率;另一方面,很好地保证了搜索过程的中间解和最终解都封闭在原离散封闭集内,有效地避开了实数集与离散集间的映射问题.基于逻辑运算的离散人工蜂群算法计算简单、易于硬件实现,在基于图论着色理论的频谱分配模型上进行验证,取得了明显优于离散人工蜂群算法的收敛速度和优化性能.

关键词: 人工蜂群算法, 离散优化, 逻辑运算, 离散封闭集, 频谱分配

Abstract:

A discrete artificial bee colony algorithm based on logic operators was proposed to avoid some defects such as high redundancy,poor exploration and trapping into a local optimization etc.By introducing a series of logical operators,the problem that solutions don't update had been solved,so the solution searching efficiency was improved.Moreover,the intermediate and final solution is enclosed in the discrete closed set,avoiding the problem that mapping the real number to discrete set.The complexity of the discrete artificial bee colony algorithm based on logic operation is lower,and it is easily implemented in hardware.The proposed algorithm is validated on the spectrum allocation model based on graph coloring,obtaining a faster convergence speed and a higher optimized performance.

Key words: artificial bee colony(ABC), discrete optimization, logic operation, discrete closed set, spectrum allocation

中图分类号:

TP18

朱冰莲, 朱方方, 苏红宏, 石锐, 段青言, 李晶. 基于逻辑运算的离散人工蜂群算法[J]. 电子学报, 2015, 43(11): 2161-2166.

ZHU Bing-lian, ZHU Fang-fang, SU Hong-hong, SHI Rui, DUAN Qing-yan, LI Jing. Discrete Artificial Bee Colony Algorithm Based on Logic Operation[J]. Acta Electronica Sinica, 2015, 43(11): 2161-2166.

参考文献

[1] Karaboga D,Akay B.A modified artificial bee colony(ABC) algorithm for constrained optimization problems[J]. Applied Soft Computing,2011,11(3):3021-3031.
[2] Gao Wei-feng,Liu San-yang.Improved artificial bee colony algorithm for global optimization[J]. Information Processing Letters,2011,111(17):871-882.
[3] Gazi V,Passino K M.Swarm Stability and Optimization[M]. Springer,2011.3-14.
[4] Karaboga D,Basturk B.On the performance of artificial bee colony(ABC) algorithm[J]. Applied Soft Computing,2008,8(1):687-697.
[5] 高卫峰,刘三阳,黄玲玲.受启发的人工蜂群算法在全局优化问题中的应用[J]. 电子学报,2012(12):2396-2403. Gao Wei-feng,Liu San-yang,Huang Ling-ling.Inspired artificial bee colony algorithm for global optimization problems[J]. Acta Electronica Sinica,2012(12):2396-2403.(in Chinese)
[6] 王生生,杨娟娟,柴胜.基于混沌鲶鱼效应的人工蜂群算法及应用[J]. 电子学报,2014(9):1731-1737. Wang Sheng-sheng,Yang Juan-juan,Chai Sheng.Artificial bee colony algorithm with chaotic catfish effect and its application[J]. Acta Electronica Sinica,2014(9):1731-1737.(in Chinese)
[7] Karaboga D,Gorkemli B,Ozturk C,et al.A comprehensive survey:artificial bee colony(ABC) algorithm and applications[J]. Artificial Intelligence Review,2012:1-37.
[8] J Krause,J Cordeiro,R S Parpinelli,et al.A survey of swarm algorithms applied to discrete optimization problems[J]. Swarm Intelligence and Bio-inspired Computation,2013:169-191.
[9] Kennedy J,Eberhart R C.A discrete binary version of the particle swarm algorithm[A]. Proceedings of IEEE International Conference on Systems,Man,and Cybernetics[C]. Bscataway,NJ:IEEE,1997:4104-4108.
[10] Engelbrecht A P,Pampara G.Binary differential evolution strategies[A]. Proceedings of the Congress on Evolutionary Computation[C]. Singapore:IEEE,2007:1942-1947.
[11] Marinakis Y,Marinaki M,Matsatsinis N.A hybrid discrete artificial bee colony-GRASP algorithm for clustering[A]. Proceedings of the International Conference on Computers & Industrial Engineering[C]. IEEE,2009:548-553.
[12] Akay B.A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding[J]. Applied Soft Computing,2013,13(6):3066-3091.
[13] ?repinšek M,Liu S,Mernik M.Exploration and exploitation in evolutionary algorithms:a survey[J]. ACM Computing Surveys(CSUR),2013,45(3):35.
[14] Peng Chun-yi,Zheng Hai-tao,Zhao Ben-Y.Utilization and fairness in spectrum assignment for opportunistic spectrum access[J]. ACM Mobile Networks and Applications(MONET),2006,11(4):555-576.
[15] D Karaboga,B Gorkemli.A combinatorial artificial bee colony algorithm for traveling salesman problem[A]. Proceedings of the International Symposium on Innovations in Intelligent Systems and Applications(INISTA)[C]. IEEE,2011.50-53.
[16] 李鑫滨,刘磊,马锴.基于离散人工蜂群算法的认知无线电频谱分配[J]. 系统工程与电子技术,2012,34(10):2136-2141. Li Xin-bin,Liu Lei,Ma Kai.Cognitive radio spectrum allocation based on discrete artificial bee colony algorithm[J]. Journal of Systems Engineering and Electronics 2012,34(10):2136-2141.(in Chinese)
[17] BRENT,R P.Fast multiple-precision evaluation of elementary functions[J]. Journal of the ACM,1976,23(2):242-251.
[18] 王钦辉,叶保留,田宇,等.认知无线电网络中频谱分配算法[J]. 电子学报.2012,40(1):147-154. Wang Qin-hui,Ye Bao-liu,Tian Yu,et al.Survey on spectrum allocation algorithms for cognitive radio networks[J]. Acta Electronica Sinica,2012,40(1):147-154.(in Chinese)
[19] Zhao Zhi-jin,Peng Zhen,Zheng Shi-lian,et al.Cognitive radio spectrum allocation using evolutionary algorithms[J]. IEEE Transactions on Wireless Communications,2009,8(9):4421-4425.

基于逻辑运算的离散人工蜂群算法

Discrete Artificial Bee Colony Algorithm Based on Logic Operation

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