[1] 董小龙， 胡修棉， 赖文. 雅鲁藏布江砂粒显微图像数据集［J］. 中国科学数据， 2020， 5（3）： 34-43. doi: 10.11922/csdata.2020.0051.zhDONGX L， HUX M， LAIW. A photomicrograph dataset of sand grains from the Yarlung Tsangpo， Tibet［J］. China Scientific Data， 2020， 5（3）： 34-43.（in Chinese）. doi: 10.11922/csdata.2020.0051.zh

[2] K K D RAMESH, G KUMAR, K SWAPNA et al. A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 169184(2018).

[3] S YARMOHAMMADI, D A WOOD, A KADKHODAIE. Reservoir microfacies analysis exploiting microscopic image processing and classification algorithms applied to carbonate and sandstone reservoirs. Marine and Petroleum Geology, 121, 104609(2020).

[4] H SAFARI, B J BALCOM, A AFROUGH. Characterization of pore and grain size distributions in porous geological samples - an image processing workflow. Computers and Geosciences, 156, 104895(2021).

[5] Z H CHEN, X J LIU, J J YANG et al. Deep learning-based method for SEM image segmentation in mineral characterization， an example from Duvernay Shale samples in Western Canada Sedimentary Basin. Computers and Geosciences, 138, 104450(2020).

[6] S PARE, A KUMAR, G K SINGH et al. Image segmentation using multilevel thresholding： a research review. Iranian Journal of Science and Technology， Transactions of Electrical Engineering, 44, 1-29(2020).

[7] G LUO, S K PAN, Y L ZHANG et al. Research on establishing numerical model of geo material based on CT image analysis. EURASIP Journal on Image and Video Processing, 2019, 36(2019).

[8] C C WANG, H ZHAO, G L SHENG et al. Multi-scale and multi-region pore structure analysis on sandy conglomerate whole core with digital rock model. Journal of Energy Resources Technology, 145(2023).

[9] M WANG, W WANG, S FENG et al. Adaptive multi-class segmentation model of aggregate image based on improved sparrow search algorithm. KSII Transactions on Internet and Information Systems, 17, 391-411(2023).

[10] 杨蕴， 李玉， 赵泉华. 高分辨率全色遥感图像多级阈值分割［J］. 光学 精密工程， 2020， 28（10）： 2370-2383. doi: 10.37188/OPE.20202810.2370YANGY， LIY， ZHAOQ H. Multi-level threshold segmentation of high-resolution panchromatic remote sensing imagery［J］. Opt. Precision Eng.， 2020， 28（10）： 2370-2383.（in Chinese）. doi: 10.37188/OPE.20202810.2370

[11] C OUCHICHA, O AMMOR, M MEKNASSI. A new approach based on exponential entropy with modified kernel fuzzy c-means clustering for MRI brain segmentation. Evolutionary Intelligence, 16, 651-665(2023).

[12] H SINGH GILL, B SINGH KHEHRA, A SINGH et al. Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values. Egyptian Informatics Journal, 20, 11-25(2019).

[13] Z ZHENG, B T ZHA, Y S XUCHEN et al. Adaptive edge detection algorithm based on grey entropy theory and textural features. IEEE Access, 7, 92943-92954(2943).

[14] Y WANG, G B ZHANG, X F ZHANG. Multilevel image thresholding using tsallis entropy and cooperative pigeon-inspired optimization bionic algorithm. Journal of Bionic Engineering, 16, 954-964(2019).

[15] H ZHANG, J E FRITTS, S A GOLDMAN. An entropy-based objective evaluation method for image segmentation, 38-49(2003).

[16] 张坤华， 谭志恒， 李斌. 结合粒子群优化和综合评价的脉冲耦合神经网络图像自动分割［J］. 光学 精密工程， 2018， 26（4）： 962-970. doi: 10.3788/ope.20182604.0962ZHANGK H， TANZ H， LIB. Automated image segmentation based on pulse coupled neural network with partide swarm optimization and comprehensive evaluation［J］. Opt. Precision Eng.， 2018， 26（4）： 962-970.（in Chinese）. doi: 10.3788/ope.20182604.0962

[17] K G DHAL et al. Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation. Archives of Computational Methods in Engineering, 27, 855-888(2020).

[18] S MIRJALILI, A H GANDOMI, S Z MIRJALILI et al. Salp swarm algorithm： a bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191(2017).

[19] A K BAIRWA, S JOSHI, D SINGH. Dingo optimizer： a nature-inspired metaheuristic approach for engineering problems. Mathematical Problems in Engineering, 2021, 1-2(2021).

[20] M BRAIK, A HAMMOURI, J ATWAN et al. White Shark Optimizer： a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowledge-Based Systems, 243, 108457(2022).

[21] L XIE, T HAN, H ZHOU et al. Tuna swarm optimization： a novel swarm-based metaheuristic algorithm for global optimization. Computational Intelligence and Neuroscience, 2021, 9210050(2021).

[22] S MIRJALILI. SCA： a Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133(2016).

[23] M BRAIK, M H RYALAT, H AL-ZOUBI. A novel meta-heuristic algorithm for solving numerical optimization problems： Ali Baba and the forty thieves. Neural Computing and Applications, 34, 409-455(2022).

[24] R G XIAO, G Q LIU, D R YI et al. Study on prediction model of liquid holdup based on back propagation neural network optimized by tuna swarm algorithm. Energy Sources， Part A： Recovery， Utilization， and Environmental Effects, 45, 8623-8641(2023).

[25] Z B QIN, H XU, Y JIN et al. Multi-strategy improved tuna swarm optimization algorithm for feature selection of network intrusion detection, 12717, 681-686(2023).

[26] B NANDA, B P MUNI, R K JENA. Enhancing power quality in microgrids with hybrid tuna-glowworm swarm optimization strategy for renewable energy sources. Energy Technology, 12, 2300067(2024).

[27] H A ALSATTAR, A A ZAIDAN, B B ZAIDAN. Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53, 2237-2264(2020).

[28] B ABDOLLAHZADEH, F S GHAREHCHOPOGH, S MIRJALILI. African vultures optimization algorithm： a new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408(2021).

[29] T GOTTHANS, J C SPROTT, J PETRZELA. Simple chaotic flow with circle and square equilibrium. International Journal of Bifurcation and Chaos, 26, 1650137(2016).

[30] M J ZHANG, D Y LONG, T QIN et al. A chaotic hybrid butterfly optimization algorithm with particle swarm optimization for high-dimensional optimization problems. Symmetry, 12, 1800(2020).

[31] W TUERXUN, C XU, H Y GUO et al. An ultra-short-term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition. Energy Science & Engineering, 10, 3001-3022(2022).

[32] C KUMAR, D MAGDALIN MARY. A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules. Optik, 264, 169379(2022).

[33] E H HOUSSEIN, G M MOHAMED, I A IBRAHIM et al. An efficient multilevel image thresholding method based on improved heap-based optimizer. Scientific Reports, 13, 9094(2023).