| Citation: | TANG Y Q，LI C H，SONG Y F，et al. Adaptive mutation sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：681-692 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0282
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To address the problems that the sparrow search algorithm is prone to fall into local extremum points in the early stage and not high accuracy in the later stage of the search, an adaptive variational sparrow search algorithm (AMSSA) is proposed. Firstly, the initial population is initialized by cat mapping chaotic sequences to enhance the randomness and ergodicity of the initial population and improve the global search ability of the algorithm; Secondly, the Cauchy mutation and Tent chaos disturbance are introduced to expand the local search ability, so that the individuals caught in the local extremum can jump out of the limit and continue the search. Finally, the explorer-follower number adaptive adjustment strategy the adaptive adjustment strategy of explorer-follower number is proposed to enhance the global search ability in the early stage and the local depth mining ability in the later stage of the algorithm by using the change of the explorer and follower numbers in each stage to improve the optimization-seeking accuracy of the algorithm. Sixteen benchmark functions and the Wilcoxon test are selected for validation, and the experimental results show that the AMSSA achieves greater improvement in search accuracy, convergence speed and stability compared with other algorithms.
| [1] |
YANG L N, SUN X, LI Z L. An efficient framework for remote sensing parallel processing: Integrating the artificial bee colony algorithm and multiagent technology[J]. Remote Sensing, 2019, 11(2): 152. doi: 10.3390/rs11020152
|
| [2] |
SHAFFIEE HAGHSHENAS S, PIROUZ B, SHAFFIEE HAGHSHENAS S, et al. Prioritizing and analyzing the role of climate and urban parameters in the confirmed cases of COVID-19 based on artificial intelligence applications[J]. International Journal of Environmental Research and Public Health, 2020, 17(10): 3730. doi: 10.3390/ijerph17103730
|
| [3] |
DASH J, DAM B, SWAIN R. Implementation of narrow-width automatic digital tuner using popular swarm intelligence technique[J]. Engineering Applications of Artificial Intelligence, 2019, 79: 87-99. doi: 10.1016/j.engappai.2018.12.009
|
| [4] |
HU P, PAN J S, CHU S C. Improved binary grey wolf optimizer and its application for feature selection[J]. Knowledge-Based Systems, 2020, 195: 105746. doi: 10.1016/j.knosys.2020.105746
|
| [5] |
KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of International Conference on Neural Networks. Piscataway: IEEE Press, 1995 : 1942-1948.
|
| [6] |
MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61. doi: 10.1016/j.advengsoft.2013.12.007
|
| [7] |
MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67. doi: 10.1016/j.advengsoft.2016.01.008
|
| [8] |
MIRJALILI S, GANDOMI A H, MIRJALILI S Z. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114: 163-191. doi: 10.1016/j.advengsoft.2017.07.002
|
| [9] |
MIRJALILI S. SCA: A sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133. doi: 10.1016/j.knosys.2015.12.022
|
| [10] |
XUE J K, SHEN B. A novel swarm intelligence optimization approach: Sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.
|
| [11] |
陈忠云, 张达敏, 辛梓芸. 多子群的共生非均匀高斯变异樽海鞘群算法[J]. 自动化学报, 2022, 48(5): 1307-1317. doi: 10.16383/j.aas.c190684
CHEN Z Y, ZHANG D M, XIN Z Y. Multi-subpopulation based symbiosis and non-uniform Gaussian mutation salp swarm algorithm[J]. Acta Automatica Sinica, 2022, 48(5): 1307-1317(in Chinese). doi: 10.16383/j.aas.c190684
|
| [12] |
刘景森, 袁蒙蒙, 左方. 面向全局搜索的自适应领导者樽海鞘群算法[J]. 控制与决策, 2021, 36(9): 2152-2160. doi: 10.13195/j.kzyjc.2020.0090
LIU J S, YUAN M M, ZUO F. Global search-oriented adaptive leader salp swarm algorithm[J]. Control and Decision, 2021, 36(9): 2152-2160(in Chinese). doi: 10.13195/j.kzyjc.2020.0090
|
| [13] |
周娇, 王力, 陈小青. 基于改进鲸鱼优化算法的最大2维熵图像分割[J]. 激光技术, 2021, 45(3): 378-385. doi: 10.7510/jgjs.issn.1001-3806.2021.03.020
ZHOU J, WANG L, CHEN X Q. Image segmentation of 2-D maximum entropy based on the improved whale optimization algorithm[J]. Laser Technology, 2021, 45(3): 378-385(in Chinese). doi: 10.7510/jgjs.issn.1001-3806.2021.03.020
|
| [14] |
周璟. 混沌狼群围捕算法的车间机器人导航路径规划[J]. 机械设计与制造, 2020(1): 251-255. doi: 10.19356/j.cnki.1001-3997.2020.01.062
ZHOU J. Workshop used robot navigation path planning method based on chaotic wolf pack besieging algorithm[J]. Machinery Design & Manufacture, 2020(1): 251-255(in Chinese). doi: 10.19356/j.cnki.1001-3997.2020.01.062
|
| [15] |
YAN C, LI M X, LIU W. Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network[J]. Applied Soft Computing, 2020, 92: 106259. doi: 10.1016/j.asoc.2020.106259
|
| [16] |
LI X G, HAN S F, GONG C Q. Analysis and improvement of fireworks algorithm[J]. Algorithms, 2017, 10(1): 26. doi: 10.3390/a10010026
|
| [17] |
何庆, 林杰, 徐航. 混合柯西变异和均匀分布的蝗虫优化算法[J]. 控制与决策, 2021, 36(7): 1558-1568. doi: 10.13195/j.kzyjc.2019.1609
HE Q, LIN J, XU H. Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm[J]. Control and Decision, 2021, 36(7): 1558-1568(in Chinese). doi: 10.13195/j.kzyjc.2019.1609
|
| [18] |
吕鑫, 慕晓冬, 张钧, 等. 混沌麻雀搜索优化算法[J]. 北京航空航天大学学报, 2021, 47(8): 1712-1720. doi: 10.13700/j.bh.1001-5965.2020.0298
LYU X, MU X D, ZHANG J, et al. Chaos sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1712-1720(in Chinese). doi: 10.13700/j.bh.1001-5965.2020.0298
|
| [19] |
LIU G Y, SHU C, LIANG Z W, et al. A modified sparrow search algorithm with application in 3d route planning for UAV[J]. Sensors, 2021, 21(4): 1224. doi: 10.3390/s21041224
|
| [20] |
YUAN J H, ZHAO Z W, LIU Y P, et al. DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm[J]. IEEE Access, 2021, 9: 16623-16629. doi: 10.1109/ACCESS.2021.3052960
|
| [21] |
CHEN S R, WANG S Y. An optimization method for an integrated energy system scheduling process based on NSGA-II improved by tent mapping chaotic algorithms[J]. Processes, 2020, 8(4): 426. doi: 10.3390/pr8040426
|
| [22] |
张娜, 赵泽丹, 包晓安, 等. 基于改进的Tent混沌万有引力搜索算法[J]. 控制与决策, 2020, 35(4): 893-900. doi: 10.13195/j.kzyjc.2018.0795
ZHANG N, ZHAO Z D, BAO X A, et al. Gravitational search algorithm based on improved Tent chaos[J]. Control and Decision, 2020, 35(4): 893-900(in Chinese). doi: 10.13195/j.kzyjc.2018.0795
|
| [23] |
郭振洲, 王平, 马云峰, 等. 基于自适应权重和柯西变异的鲸鱼优化算法[J]. 微电子学与计算机, 2017, 34(9): 20-25. doi: 10.19304/j.cnki.issn1000-7180.2017.09.005
GUO Z Z, WANG P, MA Y F, et al. Whaleoptimization algorithm based on adaptive weight and cauchy mutation[J]. Microelectronics & Computer, 2017, 34(9): 20-25(in Chinese). doi: 10.19304/j.cnki.issn1000-7180.2017.09.005
|
| [24] |
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. doi: 10.1016/j.swevo.2011.02.002
|
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