Improved Dung Beetle Optimization Algorithm with Multi-strategy
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    Abstract:

    An improved dung beetle optimization algorithm integrating multiple strategies (MSDBO) is proposed to solve the problems of weak global exploration ability, low convergence accuracy, and easy capture by local optimum solution. Firstly, this study introduces the social learning strategy to guide the dung beetle to update its position, which improves the global exploration ability of the algorithm and avoids the algorithm falling into local optimal. Secondly, the study proposes a direction-following strategy to establish the interaction between the thief and the ball-rolling dung beetle, which improves the accuracy of optimization. Finally, taking into account the performance and time consumption, it introduces environment-aware probability to guide the thief to adopt the direction-following strategy reasonably. Several optimization algorithms are selected and compared with MSDBO. By solving and analyzing 12 benchmark test functions, it is proved that the optimization performance of MSDBO is significantly better than that of the comparison algorithm. The results of pressure vessel design optimization verify the effectiveness of MSDBO in solving practical engineering constraint optimization problems.

    Reference
    [1] Yang XS. Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 2020, 46: 101104.
    [2] Holland JH. Genetic algorithms. Scientific American, 1992, 267(1): 66–73.
    [3] Storn R, Price K. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341–359.
    [4] Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, et al. Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 2016, 26: 8–22.
    [5] Hashim FA, Hussain K, Houssein EH, et al. Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 2021, 51(3): 1531–1551.
    [6] Neggaz N, Houssein EH, Hussain K. An efficient henry gas solubility optimization for feature selection. Expert Systems with Applications, 2020, 152: 113364.
    [7] Mousavirad SJ, Ebrahimpour-Komleh H. Human mental search: A new population-based metaheuristic optimization algorithm. Applied Intelligence, 2017, 47: 850–887.
    [8] Emami H. Stock exchange trading optimization algorithm: A human-inspired method for global optimization. The Journal of Supercomputing, 2022, 78(2): 2125–2174.
    [9] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46–61.
    [10] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95: 51–67.
    [11] Heidari AA, Mirjalili S, Faris H, et al. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 2019, 97: 849–872.
    [12] Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67–82.
    [13] Seyyedabbasi A, Kiani F. Sand cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 2023, 39(4): 2627–2651.
    [14] Zhao SJ, Zhang TR, Ma SL, et al. Sea-horse optimizer: A novel nature-inspired meta-heuristic for global optimization problems. Applied Intelligence, 2023, 53(10): 11833–11860.
    [15] Xue JK, Shen B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 2023, 79(7): 7305–7336.
    [16] 李晴. 基于紫外-可见光谱法的水质COD在线监测系统设计[硕士学位论文]. 绵阳: 西南科技大学, 2023.
    [17] 董奕含, 喻志超, 胡天跃, 等. 基于改进蜣螂优化算法的瑞雷波频散曲线反演方法. 油气地质与采收率, 2023, 30(4): 86–97.
    [18] 潘志远, 卜凡亮. 基于蜣螂算法优化的DV-Hop定位算法. 电子测量与仪器学报, 2023, 37(7): 33–41.
    [19] Zhu F, Li GS, Tang H, et al. Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Systems with Applications, 2024, 236: 121219.
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王乐遥,顾磊.多策略融合改进的蜣螂优化算法.计算机系统应用,2024,33(2):224-231

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History
  • Received:August 10,2023
  • Revised:September 09,2023
  • Online: December 18,2023
  • Published: February 05,2023
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