Aiming at the shortcomings of artificial bee colony algorithm in previous studies, such as exploration limitations and development inefficiency, an improved artificial bee colony algorithm with adaptive convergence is proposed. The algorithm uses global sampling and random initialization to ensure the integrity of the initial solution set. The mining times factor is added to the selection probability calculation to increase the probability of potential solutions. Combining the characteristics of the cosine function change, the selected individuals are subjected to adaptive partial development under the guidance of the global optimal individual to improve the accuracy of local development. Finally, through comparison with multiple algorithms in different disaster scenarios, the results show that the improved algorithm has higher solution accuracy, better global convergence, and can efficiently solve path planning problems in complex disaster scenarios.
ZHU Jinlei
,
YUAN Xiaobing
,
PEI Jun
. Path planning in disaster scenarios based on improved artificial bee colony algorithm[J]. Journal of University of Chinese Academy of Sciences, 2023
, 40(3)
: 397
-405
.
DOI: 10.7523/j.ucas.2021.0027
[1] Li X M, Huang Y H, Zhou Y J, et al. Robot path planning using improved artificial bee colony algorithm[C]//2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). October 12-14, 2018, Chongqing, China. IEEE, 2018:603-607.DOI:10.1109/IAEAC.2018.8579242.
[2] Li B, Chiong R, Lin M. A two-layer optimization framework for UAV path planning with interval uncertainties[C]//2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS). December 9-12, 2014, Orlando, FL, USA. IEEE, 2014:120-127.DOI:10.1109/CIPLS.2014.7007170.
[3] Li B, Gong L G, Yang W L. An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning[J]. The Scientific World Journal, 2014, 2014:232704.DOI:10.1155/2014/232704.
[4] Li B, Gong L G, Zhao C H. Unmanned combat aerial vehicles path planning using a novel probability density model based on Artificial Bee Colony algorithm[C]//2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP). June 9-11, 2013, Beijing, China. IEEE, 2013:620-625.DOI:10.1109/ICICIP.2013.6568149.
[5] Murrieta-Mendoza A, Botez R M, Bunel A. Four-dimensional aircraft en route optimization algorithm using the artificial bee colony[J]. Journal of Aerospace Information Systems, 2018, 15(6):307-334.DOI:10.2514/1.1010523.
[6] Tian G Z, Zhang L, Bai X, et al. Real-time dynamic track planning of multi-UAV formation based on improved artificial bee colony algorithm[C]//201837th Chinese Control Conference (CCC). July 25-27, 2018. Wuhan. IEEE, 2018:10055-10060.
[7] Liu H, Xu B, Lu D J, et al. A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm[J]. Applied Soft Computing, 2018, 68:360-376.DOI:10.1016/j.asoc.2018.04.015.
[8] Patle B K, Babu L G, Pandey A, et al. A review:on path planning strategies for navigation of mobile robot[J]. Defence Technology, 2019, 15(4):582-606.DOI:10.1016/j.dt.2019.04.011.
[9] Ren G, Wu J B, Versonnen F. Bee-based reliable data collection for mobile wireless sensor network[J]. Cluster Computing, 2019, 22(4):9251-9260.DOI:10.1007/s10586-018-2116-0.
[10] Zhou Y Q, Liu L, Wang L, et al. Service-aware 6G:an intelligent and open network based on the convergence of communication, computing and caching[J]. Digital Communications and Networks, 2020, 6(3):253-260.DOI:10.1016/j.dcan.2020.05.003.
[11] Zhou Y Q, Tian L, Liu L, et al. Fog computing enabled future mobile communication networks:a convergence of communication and computing[J]. IEEE Communications Magazine, 2019, 57(5):20-27.DOI:10.1109/MCOM.2019.1800235.
[12] Li Z Y, Zhang Z, Liu H, et al. A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm[J]. International Journal of Advanced Robotic Systems, 2020, 17(1):172988141989478.DOI:10.1177/1729881419894787.
[13] Gao W F, Liu S Y, Huang L L. A novel artificial bee colony algorithm based on modified search equation and orthogonal learning[J]. IEEE Transactions on Cybernetics, 2013, 43(3):1011-1024.DOI:10.1109/TSMCB.2012.2222373.
[14] Zhang H Y, Lin W M, Chen A X. Path planning for the mobile robot:a review[J]. Symmetry, 2018, 10(10):450.DOI:10.3390/sysm10100450.
[15] Zhang Q, Wei S, Xue M, et al. A variable-step two synchronous artificial bee colony algorithm for mobile robot path planning[C]//2018 Chinese Automation Congress (CAC). November 30-December 2, 2018, Xi'an, China. IEEE, 2018:2054-2058.DOI:10.1109/CAC.2018.8623333.
[16] 刘东林,陈银银. 基于花香浓度的人工蜂群算法在机器人路径规划中的应用[J]. 华东理工大学学报(自然科学版), 2016, 42(3):375-381.DOI:10.14135/j.cnki.1006-3080.2016.03.013.
[17] Gao W F, Liu S Y. A modified artificial bee colony algorithm[J]. Computers & Operations Research, 2012, 39(3):687-697.DOI:10.1016/j.cor.2011.06.007.
[18] Wang J C, Sun Y H, Liu F X. An improved double-population artificial bee colony algorithm based on heterogeneous comprehensive learning[J]. Soft Computing, 2018, 22(19):6489-6514.DOI:10.1007/s00500-017-2700-x.
[19] Singh A, Deep K. Artificial bee colony algorithm with improved search mechanism[J]. Soft Computing, 2019, 23(23):12437-12460.DOI:10.1007/s00500-019-03785-y.
[20] Lu R, Hu H D, Xi M L, et al. An improved artificial bee colony algorithm with fast strategy, and its application[J]. Computers & Electrical Engineering, 2019, 78:79-88.DOI:10.1016/j.compeleceng.2019.06.021.
[21] Yi Y, He R J. A novel artificial bee colony algorithm[C]//2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics. August 26-27, 2014, Hangzhou, China. IEEE, 2014:271-274.DOI:10.1109/IHMSC.2014.73.
[22] Xu F Y, Li H L, Pun C M, et al. A new global best guided artificial bee colony algorithm with application in robot path planning[J]. Applied Soft Computing, 2020, 88:106037.DOI:10.1016/j.asoc.2019.106037.
[23] Alpkiray N, Torun Y, Kaynar O. Probabilistic roadmap and artificial bee colony algorithm cooperation for path planning[C]//2018 International Conference on Artificial Intelligence and Data Processing (IDAP). September 28-30, 2018, Malatya, Turkey. IEEE, 2018:1-6.DOI:10.1109/IDAP.2018.8620808.
[24] Yue T S, Chung H Y. Using ABC and RRT algorithms to improve mobile robot path planning with danger degree[C]//2016 Fifth International Conference on Future Generation Communication Technologies (FGCT). August 17-19, 2016, London, UK. IEEE, 2016:21-26.DOI:10.1109/FGCT.2016.7605068.
