Artificial Bee Colony Algorithm with Gradually Enhanced Exploitation

doi:10.16183/j.cnki.jsjtu.2018.01.015

Abstract

Abstract: Concerning the issue that solution search equation of artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation, a new method is proposed to gradually enhance the exploitation ability of ABC algorithm. In the proposed algorithm, employed bees learn from the local best individuals and proportion of the local best individuals is gradually enhanced. Onlooker bees learn from the local best and global best individuals and proportion of the global best individual is gradually enhanced. A forementioned measures can effectively balance the exploration and exploitation ability of ABC algorithm. The experiments are conducted on 36 benchmark functions, including CEC2014 problems. The performance of the proposed algorithm outperforms those of ABC with neighborhood search (ABC-NS) and composite DE (CoDE) algorithms significantly.

Key words: artificial bee colony (ABC) algorithm; global best; local best; balance

CLC Number:

TP 18

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.

References

［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.

[1]	BAI Xiongfei, GONG Shuicheng, LI Xuesong, XU Bo, YANG Xiaoli, WANG Mingyan. Defect Classification of Weld Metallographic Structure Based on Data Augmentation of Poisson Fusion [J]. Journal of Shanghai Jiao Tong University, 2023, 57(10): 1316-1328.
[2]	LI Shaoyuan, YIN Xiang. Do Agent Self-Game and Learning Have Limitation of Performance? [J]. Journal of Shanghai Jiao Tong University, 2021, 55(Sup.1): 3-4.
[3]	FAN Houming,XU Zhenlin,LI Yang,LIU Wenqi,GENG Jing. Hybrid Genetic Algorithm for Solving Multi-Depot Joint Distribution Routing Problem [J]. Journal of Shanghai Jiaotong University, 2019, 53(8): 1000-1009.
[4]	LIU Min,ZHANG Yingtang,LI Zhining,FAN Hongbo. Diesel Engine Fault Online Diagnosis Method Based on Incremental Sparse Kernel Extreme Learning Machine [J]. Journal of Shanghai Jiaotong University, 2019, 53(2): 217-224.
[5]	BI Xiaojun,HU Songyi. Firefly Algorithm with High Precision Mixed Strategy Optimized Particle Filter [J]. Journal of Shanghai Jiaotong University, 2019, 53(2): 232-238.
[6]	LU Chengbo，MEI Ying. An Accurate and Robust Online Sequential Learning Algorithm for Feedforward Networks [J]. Journal of Shanghai Jiaotong University, 2015, 49(08): 1137-1143.
[7]	WANG Ping,WANG Di,FENG Wei. Online Semi-Supervised Extreme Learning Machine Based on Manifold Regularization [J]. Journal of Shanghai Jiaotong University, 2015, 49(08): 1153-1158.
[8]	BAO Zhiguo,WAN Jinliang,MA Xifeng. Optimization Design of FaultTolerant Image Filter Based on Genetic Algorithm [J]. Journal of Shanghai Jiaotong University, 2015, 49(08): 1181-1185.
[9]	JI Liangbo. Precision Prediction Model in Fused Deposition Modeling of Three-Dimensional Printing Based on Wavelet Neural Network [J]. Journal of Shanghai Jiaotong University, 2015, 49(03): 375-378.
[10]	WANG Xiaoxia,YANG Fengbao,LIN Suzhen,SHI Dongmei. A Method of Risk Assessment Based on Flexible Similarity Measurement and Possibility Skewness [J]. Journal of Shanghai Jiaotong University, 2014, 48(10): 1440-1445.
[11]	YANG Gang1, 2,QIAO Junfei1. Analysis of Span-Lateral Inhibition Neural Network and Its Application [J]. Journal of Shanghai Jiaotong University, 2014, 48(07): 965-970.
[12]	YANG Jing1,KONG Bin1,WANG Bin2. Sparsely Connected Associative Memory Based on the Preferential Mechanism of Heuristic Annealed Topology [J]. Journal of Shanghai Jiaotong University, 2013, 47(07): 1009-1014.
[13]	WANG Li1,2,ZHOU Xianzhong1,LI Huaxiong1. Fuzzy Decision-theoretic Rough Set Model and Its Attribute Reduction [J]. Journal of Shanghai Jiaotong University, 2013, 47(07): 1032-1035.
[14]	WU Wentong1,LI Yuanxiang1，Wei Banghe2,Zheng Silong1. Incremental ISOMAP Method Based on Locally Estimated Geodesic Distance [J]. Journal of Shanghai Jiaotong University, 2013, 47(07): 1082-1087.
[15]	LEI Ju- Yang. Speech Enhancement Based on Chinese Restaurant Process [J]. Journal of Shanghai Jiaotong University, 2013, 47(04): 635-639.