Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (6): 98-108.doi: 10.16088/j.issn.1001-6600.2021122104

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An Improved Slime Mould Algorithm Based on Multi-Strategy

WANG Ximin, YUAN Jie*, KOU Qiaoyuan   

  1. School of Electrical Engineering, Xinjiang University, Urumqi Xinjiang 830017, China
  • Received:2021-12-21 Revised:2022-03-09 Online:2022-11-25 Published:2023-01-17

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Abstract: Aiming at the problem of low search efficiency and local optimum of Slime Mould Algorithm (SMA), an improved multi-strategy Slime Mould Algorithm is proposed. Firstly, the optimal population is obtained by Tent mapping reverse learning strategy as the initial population to improve the convergence speed of the algorithm. Secondly, slime molds updates location through adaptive weight strategy and disturbance strategy, adjusts the exploration and development ability of the algorithm, avoids falling into premature, and improves the convergence speed. Finally, compared with four classical algorithms including PSO, WOA, GWO, SMA and a related improved SMA algorithm, CEC test function is tested. The optimization results show that the improved algorithm has strong search efficiency and ability to avoid falling into local optimum. The algorithm can find the global optimal value in a short period of time, which are more effective and can improves the convergence speed and convergence accuracy of the test function to some degrees.

Key words: Tent map, reverse learning strategy, adaptive weight strategy, perturbation strategy, slime molds algorithm

CLC Number: 

  • TP18
[1] LIN L, GEN M. Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation[J]. Soft Computing, 2009, 13(2): 157-168. DOI: 10.1007/s00500-008-0303-2.
[2] 王国宇, 黄植功, 戴明. 基于改进粒子群算法的无刷电机模糊控制研究[J]. 广西师范大学学报(自然科学版), 2016, 34(2): 21-27. DOI: 10.16088/j.issn.1001-6600.2016.02.004.
[3] 逯苗, 何登旭, 曲良东. 非线性参数的精英学习灰狼优化算法[J]. 广西师范大学学报(自然科学版), 2021, 39(4): 55-67. DOI: 10.16088/j.issn.1001-6600.2020093002.
[4] 富立琪,王华倩,乔学工.基于k-means分簇和灰狼优化的无线传感网络路由算法[J].电子设计工程,2021,29(23):1-6.DOI:10.14022/j.issn1674-6236.2021.23.001.
[5] 刘景森, 郑智远, 李煜. 一种交互演化改进鲸鱼算法及其收敛性分析[J/OL]. 控制与决策:1-9[2021-12-21]. https://doi.org/10.13195/j.kzyjc.2021.0807.
[6] 许伦辉, 陈凯勋. 基于改进萤火虫算法优化BP神经网络的路网速度分布预测[J]. 广西师范大学学报(自然科学版), 2019, 37(2): 1-8. DOI: 10.16088/j.issn.1001-6600.2019.02.001.
[7] 雷蕾,陈宏滨.基于萤火虫算法的无线可充电传感器网络的充电策略[J].桂林电子科技大学学报,2021,41(6):477-483.DOI:10.16725/j.cnki.cn45-1351/tn.2021.06.012.
[8] 许伦辉, 尹诗德, 刘易家. 基于模拟退火的自适应布谷鸟算法求解公交调度问题[J]. 广西师范大学学报(自然科学版), 2018, 36(2): 1-7. DOI: 10.16088/j.issn.1001-6600.2018.02.001.
[9] LI S M, CHEN H L, WANG M J, et al. Slime mould algorithm: a new method for stochastic optimization[J]. Future Generation Computer Systems, 2020, 111: 300-323. DOI: 10.1016/j.future.2020.03.055.
[10] CHEN H L, ZANG Q, LUO J, et al. An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine[J]. Applied Soft Computing, 2019, 86(11):105884. DOI: 10.1016/j.asoc.2019.105884.
[11] 龙洋, 苏义鑫, 廉城, 等. 混合细菌觅食算法求解无人艇路径规划问题[J]. 华中科技大学学报(自然科学版), 2022, 50(3): 68-73. DOI: 10.13245/j.hust.220313.
[12] MOSTAFA M, REZK H, ALY M, et al. A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel[J]. Sustainable Energy Technologies and Assessments, 2020, 42: 100849. DOI: 10.1016/j.seta.2020.100849.
[13] LIU Y, HEIDARI A A, YE X J, et al. Boosting slime mould algorithm for parameter identification of photovoltaic models[J]. Energy, 2021, 234: 121164. DOI: 10.1016/j.energy.2021.121164.
[14] EKINCI S, IZCI D, ZEYNELGIL H L, et al. An application of slime mould algorithm for optimizing parameters of power system stabilizer[C]// 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). Piscataway, NJ: IEEE, 2020: 530-534. DOI: 10.1109/ISMSIT50672.2020.9254597.
[15] AGARWAL D, BHARTI P S. Implementing modified swarm intelligence algorithm based on slime moulds for path planning and obstacle avoidance problem in mobile robots[J]. Applied Soft Computing, 2021, 107: 107372. DOI: 10.1016/j.asoc.2021.107372.
[16] HASSAN M H, KAMEL S, ABUALIGAH L, et al. Development and application of slime mould algorithm for optimal economic emission dispatch[J]. Expert Systems with Applications, 2021, 182: 115205. DOI: 10.1016/j.eswa.2021.115205.
[17] 翟青海,谢晓兰.混合云环境下考虑工作流的任务调度策略[J].桂林理工大学学报,2021,41(4):891-896. DOI: 10.3969/j.issn.1674-9057.2021.04.024.
[18] LIU M J, LI Y H, HUO Q, et al. A two-way parallel slime mold algorithm by flow and distance for the travelling salesman problem[J]. Applied Sciences, 2020, 10(18): 6180. DOI: 10.3390/app10186180.
[19] 肖亚宁,孙雪,李三平, 等. 基于混沌精英黏菌算法的无刷直流电机转速控制[J]. 科学技术与工程, 2021, 21(28): 12130-12138. DOI: 10.3969/j.issn.1671-1815.2021.28.028.
[20] CHEN Z Y, LIU W B. An efficient parameter adaptive support vector regression using K-means clustering and chaotic slime mould algorithm[J]. IEEE Access, 2020, 8:156851-156862. DOI:10.1109/ACCESS.2020.3018866.
[21] ZHAO J, GAO Z M, SUN W. The improved slime mould algorithm with Levy flight[J]. Journal of Physics: Conference Series, 2020, 1617: 012033. DOI: 10.1088/1742-6596/1617/1/012033.
[22] ZHAO J, GAO Z M. The hybridized Harris hawk optimization and slime mould algorithm[J]. Journal of Physics: Conference Series, 2020, 1682: 012029. DOI: 10.1088/1742-6596/1682/1/012029.
[23] GAO Z M, ZHAO J, YANG Y, et al. The hybrid grey wolf optimization-slime mould algorithm[J]. Journal of Physics: Conference Series, 2020, 1617: 012034. DOI: 10.1088/1742-6596/1617/1/012034.
[24] WU D, LIANG X D, HE M W. Orthogonal learning-based improved slime mould algorithm for global optimization[C]// 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). Piscataway, NJ: IEEE, 2021: 257-263. DOI: 10.1109/ICPICS52425.2021.9524136.
[25] HU J, GUI W Y, HEIDARI A A, et al. Dispersed foraging slime mould algorithm: continuous and binary variants for global optimization and wrapper-based feature selection[J]. Knowledge-Based Systems, 2021, 237: 107761. DOI: 10.1016/j.knosys.2021.107761.
[26] WEI Y Y, ZHOU Y Q, LUO Q F, et al. Optimal reactive power dispatch using an improved slime mould algorithm[J]. Energy Reports, 2021, 107: 8742-8759. DOI: 10.1016/j.egyr.2021.11.138.
[27] NAIK M K, PANDA R, ABRAHAM A. Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm[J]. Journal of King Saud University: Computer and Information Sciences, 2022, 34(7): 4524-4536. DOI: 10.1016/j.jksuci.2020.10.030.
[28] RIZK-ALLAH R M, HASSANIEN A E, SLOWIK A. Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization[J]. Neural Computing and Applications, 2020, 32(17): 13715- 13746. DOI: 10.1007/s00521-020-04779-w.
[29] 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.
[30] TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence[C]// International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). Piscataway, NJ: IEEE, 2005: 695-701. DOI: 10.1109/CIMCA.2005.1631345.
[31] 姜天华. 基于灰狼优化算法的低碳车间调度问题[J]. 计算机集成制造系统, 2018, 24(10): 2428-2435. DOI: 10.13196/j.cims.2018.10.006.
[32] 黄晨晨, 魏霞, 黄德启, 等.求解高维复杂函数的混合蛙跳-灰狼优化算法[J]. 控制理论与应用, 2020, 37(7): 1655-1666. DOI: 10.7641/CTA.2020.90461.
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