[1] TEODOROVIC D, LUCIC P, MARKOVIC G, et al. Bee colony optimization:principles and applications[C]//Proceedings of the 8th Seminar on Neural Network Applications in Electrical Engineering. Piscataway, NJ:IEEE, 2006:151-156. [2] NIKOLIC M, TEODOROVIC D. Empirical study of the Bee Colony Optimization (BCO) algorithm[J]. Expert Systems with Applications, 2013, 40(11):4609-4620. [3] KANG F, LI J, MA Z. Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions[J]. Information Sciences, 2011, 181(16):3508-3531. [4] 张振海, 李士宁, 李志刚, 等. 一类基于信息熵的多标签特征选择算法[J]. 计算机研究与发展, 2013, 50(6):1177-1184.(ZHANG Z H, LI S N, LI Z G, et al. Multi-label feature selection algorithm based on information entropy[J]. Journal of Computer Research and Development, 2013, 50(6):1177-1184.) [5] 谢娟英, 谢维信. 基于特征子集区分度与支持向量机的特征选择算法[J]. 计算机学报, 2014, 37(8):1704-1718.(XIE J Y, XIE W X. Several feature selection algorithms based on the discernibility of a feature subset and support vector machines[J]. Chinese Journal of Computers, 2014, 37(8):1704-1718.) [6] BOLÓN-CANEDO V, SÁNCHEZ-MAROÑO N, ALONSO-BET-ANZOS A. A review of feature selection methods on synthetic data[J]. Knowledge & Information Systems, 2013, 34(3):483-519. [7] 代旺, 方昱春, 李杨. 融合过滤和封装方式的特征选择算法[J]. 计算机工程, 2012, 38(24):166-170.(DAI W, FANG Y C, LI Yang. Feature selection algorithm fused with filtering and packaging mode[J]. Computer Engineering, 2012, 38(24):166-170.) [8] DZIWINSKI P, LUKASZ BARTCZUK, STARCZEWSKI J T. Fully controllable ant colony system for text data clustering[C]//Proceedings of the 2012 International Symposia on Swarm and Evolutionary Computation. Berlin:Springer, 2012:199-205. [9] KASHAN M H, NAHAVANDI N, KASHAN A H. DisABC:a new artificial bee colony algorithm for binary optimization[J]. Applied Soft Computing, 2012, 12(1):342-352. [10] ASAD A H, AZAR A T, HASSAANIEN A E O. A comparative study on feature selection for retinal vessel segmentation using ant colony system[C]//Proceedings of the 2nd International Symposium on Intelligent Informatics. Berlin:Springer, 2014, 235:1-11. [11] 杨鸿章.基于蚁群算法特征选择的语音情感识别[J]. 计算机仿真, 2013, 30(4):377-381.(YANG H Z. Feature selection of speech emotional recognition based on ant colony optimization algorithm[J]. Computer Simulation, 2013, 30(4):377-381.) [12] PALANISAMY S, KANMANI S. Artificial bee colony approach for optimizing feature selection[J]. International Journal of Computer Science Issues, 2012, 9(3):432-438. [13] FORSATI R, MOAYEDIKIA A, SHAMSFARD M, et al. A novel approach for feature selection based on the bee colony optimization[J]. International Journal of Computer Applications, 2012, 43(8):13-16. [14] RAKSHIT P, BHATTACHARYYA S, KONAR A, et al. Artificial bee colony based feature selection for motor imagery EEG data[C]//Proceedings of Seventh International Conference on Bio-Inspired Computing:Theories and Applications. Berlin:Springer, 2013:127-138. [15] FORSATI R, MOAYEDIKIA A, JENSEN R, et al. Enriched ant colony optimization and its application in feature selection[J]. Neurocomputing, 2014, 142(1):354-371. [16] TEODOROVIC D, LUCIC P, MARKOVIC G, et al. Bee colony optimization:principles and applications[C]//Proceedings of the 8th Seminar on Neural Network Applications in Electrical Engineering. Piscataway, NJ:IEEE, 2006:151-156. [17] ALZAQEBAH M, ABDULLAH S. Hybrid bee colony optimization for examination timetabling problems[J]. Computers & Operations Research, 2015, 54(54):142-154. [18] LU P, ZHOU J, ZHANG H, et al. Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects[J]. International Journal of Electrical Power & Energy Systems, 2014, 62(11):130-143. |