上海交通大学学报 ›› 2018, Vol. 52 ›› Issue (1): 96-102.doi: 10.16183/j.cnki.jsjtu.2018.01.015
杜振鑫1,2,韩德志2,刘广钟2,贾建鑫2
发布日期:
2018-01-01
基金资助:
DU Zhenxin1,2,HAN Dezhi2,LIU Guangzhong2,JIA Jianxin2
Published:
2018-01-01
摘要: 针对人工蜂群(ABC)算法开采能力差的问题,提出一种逐步加强开采能力的改进ABC算法.在雇佣蜂阶段,向局部最优解学习,并逐步增大局部最优解的比率;在观察蜂阶段,向局部最优解和全局最优解学习,并逐步增大全局最优解的比率,从而较好地平衡算法的勘探与开采能力.在CEC2014等36个函数上进行实验,结果表明,改进ABC算法的性能明显优于ABC-NS和CoDE等算法.
中图分类号:
杜振鑫1,2,韩德志2,刘广钟2,贾建鑫2. 一种逐步加强开采的人工蜂群算法[J]. 上海交通大学学报, 2018, 52(1): 96-102.
DU Zhenxin1,2,HAN Dezhi2,LIU Guangzhong2,JIA Jianxin2. Artificial Bee Colony Algorithm with Gradually Enhanced Exploitation[J]. Journal of Shanghai Jiao Tong University, 2018, 52(1): 96-102.
[1]KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm[J]. Applied Mathema-tics and Computation, 2009, 214(1): 108-132. [2]LI G, CUI L, FU X, et al. Artificial bee colony algorithm with gene recombination for numerical function optimization[J]. Applied Soft Computing, 2017, 52(3): 146-159. [3]CUI L, ZHANG K, LI G, et al. Modified Gbest-guided artificial bee colony algorithm with new probability model[J]. Soft Computing, 2017, 21(1): 1-27. [4]LI X, YANG G. Artificial bee colony algorithm with memory[J]. Applied Soft Computing, 2016, 41(4): 362-372. [5]PAN Q K, WANG L, LI J Q, et al. A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan mini-mization[J]. Omega, 2014, 45(6): 42-56. [6]ZORARPACL E, OZEL S A. A hybrid approach of differential evolution and artificial bee colony for feature selection[J]. Expert Systems with Applications, 2016, 62(11): 91-103. [7]SHI Y, PUN C M, HU H, et al. An improved artificial bee colony and its application[J]. Knowledge-Based Systems, 2016, 107(9): 14-31. [8]LOZANO M, GARCIA-MARTINEZ C, RODRIGUEZ F J, et al. Optimizing network attacks by artificial bee colony[J]. Information Sciences, 2017, 377(1): 30-50. [9]BOSE D, BISWAS S, VASILAKOS A V, et al. Optimal filter design using an improved artificial bee colony algorithm[J]. Information Sciences, 2014, 281(10): 443-461. [10]王文全, 黄胜, 王超. 舰船通道布置方案的直觉模糊多属性群决策方法[J]. 上海交通大学学报, 2013, 47(6): 894-899. WANG Wenquan, HUANG Sheng, WANG Chao. Intuitionistic fuzzy multiple attribute group decision-making for ship passage arrangement[J]. Journal of Shanghai Jiao Tong University, 2013, 47(6): 894-899. [11]ZHU G, KWONG S. Gbest-guided artificial bee co-lony algorithm for numerical function optimization[J]. Applied Mathematics and Computation, 2010, 217(7): 3166-3173. [12]GAO W F, LIU S Y. A modified artificial bee colony algorithm[J]. Computers & Operations Research, 2012, 39(3): 687-697. [13]GAO W, LIU S, HUANG L. Enhancing artificial bee colony algorithm using more information-based search equations[J]. Information Sciences, 2014, 270(6): 112-133. [14]GAO W, LIU S, HUANG L. A global best artificial bee colony algorithm for global optimization[J]. Journal of Computational and Applied Mathematics, 2012, 236(11): 2741-2753. [15]周新宇, 吴志健, 邓长寿, 等. 一种邻域搜索的人工蜂群算法[J]. 中南大学学报(自然科学版), 2015, 46(2): 534-546. ZHOU Xinyu, WU Zhijian, DENG Changshou, et al. Neighborhood search-based artificial bee colony algorithm[J]. Journal of Central South University (Science and Technology), 2015, 46(2): 534-546. [16]DERRAC J, GARCIA S, MOLINA D, et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J]. Swarm and Evolutionary Computation, 2011, 1(1): 3-18. [17]LYNN N, SUGANTHAN P N. Modified artificial bee colony algorithm with comprehensive learning reinitialization strategy[C]∥IEEE International Conference on Systems, Man, and Cybernetics (SMC). Singapore: IEEE, 2015: 2129-2134. [18]WANG Y, CAI Z, ZHANG Q. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 55-66. [19]LIANG J J, QU B Y, SUGANTHAN P N. Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization[R]. Singapore: Nanyang Technological University, 2013. |
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