混合策略改进的蜣螂优化算法及其工程应用
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国家自然科学基金资助项目(72174121,71774111);上海市2022年度“科技创新行动计划”软科学研究项目(22692112600);上海市自然科学基金资助项目(21ZR1444100)


Hybrid strategy improved dung beetle optimizer and its engineering application
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    摘要:

    针对原始蜣螂优化算法(dung beetle optimizer, DBO)容易陷入局部最优,收敛精度不够等问题,提出一种混合策略改进的蜣螂优化算法(TDBO)。运用Tent混沌映射策略初始化种群,使得初始蜣螂的位置分布更加均匀,提高种群的多样性;在蜣螂繁衍阶段使用自适应惯性权重,提升寻优能力;在蜣螂偷窃行为公式中引入莱维飞行,提高算法的搜索能力,使算法跳出局部最优,平横搜索多样性与收敛准确性之间的关系。在9个测试函数上分别与基础DBO算法、4种对比算法以及单一策略改进的DBO算法进行比较,并通过Wilcoxon秩和检验验证TDBO算法的性能。结果证明,TDBO算法在多个函数上速度和精度优于对比算法,并具有显著性差异。通过基准函数的测试、Wilcoxon秩和检验,以及3个工程优化问题的验证,TDBO算法具有较优的收敛精度和速度。

    Abstract:

    Aiming at the problems that the original dung beetle optimizer (DBO) is easy to prone to local optimum and the low convergence precision, a multi-strategy fusion improved dung beetle optimizer (TDBO) is proposed. Using the Tent chaos initialization population mapping strategy to makes the initial position of dung beetle distribution more uniform, and improve the diversity of population, adaptive inertia weight was applied during the breeding stage to improve the optimization ability. Levy flight was introduced into the dung beetle stealing behavior formula to improve the search ability of the algorithm, make the algorithm jump out of the local optimal, equates the relationship between diversity and convergence accuracy. Compared with the basic DBO algorithm, four comparison algorithms and single-strategy improved DBO algorithm on 9 test functions, and Wilcoxon rank sum test is used to verify the performance of the TDBO algorithm. The results show that the speed and accuracy of the TDBO algorithm are better than the comparison algorithm on multiple functions, and the TDBO algorithm has significant difference. Through the test of benchmark functions, the verification of Wilcoxon rank sum test, and validation of three engineering optimization problems, the TDBO algorithm has better convergence accuracy and speed.

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吉如沁,秦江涛.混合策略改进的蜣螂优化算法及其工程应用[J].上海理工大学学报,2024,46(5):580-588.

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  • 收稿日期:2023-04-04
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  • 在线发布日期: 2024-10-29