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多策略帝王蝶优化算法及其工程应用

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针对帝王蝶优化算法存在的收敛速度慢、寻优精度低和易陷入局部极值等缺陷,提出了一种多策略改进的帝王蝶优化算法.首先,采用正向正态云发生器对父代帝王蝶个体执行非线性云化操作,增加候选解数量,提高算法的局部开发能力;之后,引入基于凸透镜成像的反向学习策略,应用到当前最优个体上产生新的个体,提高算法收敛速度和寻优精度;最后,在调整算子中融入自适应策略,增加种群的多样性,避免算法陷入局部最优.通过对8个基准测试函数的寻优对比,以及Wilcoxon秩和检验结果的对比,发现改进算法具有更好的收敛性能、寻优性能和鲁棒性.与此同时,通过工程应用中压力容器设计和焊接梁设计的优化对比,进一步验证了改进算法处理实际工程问题时的优越性.
Improved monarch butterfly optimization algorithm and its engineering application
[Objective]In recent years,a large number of nonconvex,highly nonlinear,multimodal,and multivariable complex optimization problems have emerged in scientific and engineering technology design due to the continuous development of science and technology.Owing to their advantages such as simple programming,flexible operation,and efficient optimization,intelligent optimization algorithms have become research hotspots to address diverse complex optimization problems in engineering applications.They have been successfully used to solve practical problems such as neural networks,resource allocation,and target tracking.In this research,multiple strategies were developed to improve the existing monarch optimization algorithm to address its shortcomings,such as slow convergence speed,low optimization accuracy,and ease of falling into local extremum.[Methods]First,the forward normal cloud generator is used to perform nonlinear cloud operation on the parent monarch butterfly,increasing the number of candidate solutions and improving the local development ability of the algorithm.Subsequently,an opposition-based learning strategy based on convex lens imaging is used to the current optimal individual which is generated by normal cloud generator to generate new individuals and improve the convergence accuracy and speed of the algorithm.Finally,adaptive strategies are incorporated into the adjustment operator to diversify the population.[Results]Several experiments were performed on benchmark functions to verify the performance of the algorithm:(1)Different strategies proposed were analyzed using ablation experiments to verify their effectiveness.The results revealed that the proposed strategies can effectively improve the algorithm's performance.(2)The improved algorithm was compared with other swarm intelligent optimization algorithms,and the results revealed that the improved algorithm can achieve the best results on most test functions.(3)The improved algorithm was also compared with other improved versions of monarch optimization algorithm,and the results revealed that the improved algorithm exhibited more advantages such as fast convergence speed and high convergence precision.(4)The Wilcoxon rank sum test and Friedman test were used to verify the performance of the proposed algorithm.The results revealed that the improved algorithm is superior to other algorithms.[Conclusions]The optimization and comparison results of the pressure vessel design and welded beam design in engineering applications further verified the superiority of the improved algorithm in addressing real-world engineering problems.

monarch butterfly optimization algorithmnormal cloud modelconvex lens imagingadaptive adjustment ratepressure vessel designwelded beam design

王振宇、王磊

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西安理工大学计算机科学与工程学院,西安 710048

帝王蝶优化算法 正态云模型 凸透镜成像 自适应调整率 压力容器设计 焊接梁设计

国家自然科学基金资助项目

62176146

2024

清华大学学报(自然科学版)
清华大学

清华大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.586
ISSN:1000-0054
年,卷(期):2024.64(4)
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