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mLBOA:A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization 认领 引用 被引量：12
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作者 Sushmita Sharma Sanjoy Chakraborty +2 位作者 Apu Kumar Saha Sukanta Nama Saroj Kumar Sahoo 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第4期1161-1176,共16页
Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its... Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants. 展开更多
关键词 Butterfly optimization algorithm Lagrange interpolation Levy flight search IEEE CEC 2017 functions Engineering design problems
Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud 认领 引用
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作者 S.Sureshkumar G.K.D.Prasanna Venkatesan R.Santhosh 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期1109-1123,共15页
Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualiz... Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches. 展开更多
关键词 Cloud computing distributed denial of service intrusion detection system adaptive butterfly optimization algorithm deep neural network
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Optimized Cardiovascular Disease Prediction Using Clustered Butterfly Algorithm 认领 引用 被引量：1
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作者 Kamepalli S.L.Prasanna Vijaya J +2 位作者 Parvathaneni Naga Srinivasu Babar Shah Farman Ali 《Computers, Materials & Continua》 SCIE EI 2025年第10期1603-1630,共28页
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h... Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance. 展开更多
关键词 Cardiovascular disease prediction healthcare management system clustering RoughK-means classification butterfly optimization algorithm
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Wind Driven Butterfly Optimization Algorithm with Hybrid Mechanism Avoiding Natural Enemies for Global Optimization and PID Controller Design 认领 引用 被引量：3
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作者 Yang He Yongquan Zhou +2 位作者 Yuanfei Wei Qifang Luo Wu Deng 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第6期2935-2972,共38页
This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabil... This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind states.The algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the wind.Also,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability.Aiming at improving the explorative performance at the initial stages and later stages,the fragrance generation method was modified.To evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions.Further,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020.Finally,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter optimization.Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA. 展开更多
关键词 Butterfly Optimization Algorithm(BOA) Wind Driven Optimization(WDO) Benchmark functions Global optimization Proportional integral derivative(PID) Metaheuristic
Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm 认领 引用 被引量：4
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作者 Noor Gul Saeed Ahmed +2 位作者 Atif Elahi Su Min Kim Junsu Kim 《Computers, Materials & Continua》 SCIE EI 2022年第4期369-387,共19页
Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best asp... Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs. 展开更多
关键词 Internet of Things cognitive radio network butterfly optimization algorithm particle swarm optimization malicious users genetic algorithm
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Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems 认领 引用 被引量：2
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作者 Sushmita Sharma Nima Khodadadi +2 位作者 Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第2期819-843,共25页
This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of B... This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared quantitatively.From all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence. 展开更多
关键词 Multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance
基于GWO融合BOA算法的无线传感器节能优化研究 认领 引用
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作者 孔祥恒 顾佳晨 +1 位作者 葛伟 曹逸荣 《自动化与仪器仪表》 2026年第2期184-187,192,共4页
节能设计通过降低传感器节点的能耗延长整个网络系统的生存周期,这对于需要长时间稳定运行的无线传感器网络来说至关重要。为实现上述目的,针对基于GWO融合BOA算法的无线传感器节能优化方法展开研究。结合GWO算法与BOA算法模型定义融合... 节能设计通过降低传感器节点的能耗延长整个网络系统的生存周期,这对于需要长时间稳定运行的无线传感器网络来说至关重要。为实现上述目的,针对基于GWO融合BOA算法的无线传感器节能优化方法展开研究。结合GWO算法与BOA算法模型定义融合方案,在此基础上,规划无线传感器网络的布局形式,实现融合GWO与BOA算法的无线传感器网络优化布置。确定传感器节点分簇模式,并根据节点轮询工作机制,计算循环参数,完成无线传感器节能方法的设计。实验结果表明,应用上述算法布置无线传感器网络,传感节点的存活时间明显延长,转发数据时所需占据的网络带宽也明显下降,符合网络节能的设计初衷。 展开更多
关键词 GWO算法 BOA算法 无线传感器 节能优化 节点分簇 轮询工作机制
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基于BOA-SAM-FTA的超期服役压力管道风险评估 认领 引用
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作者 唐华彬 许磊 +1 位作者 陈思宇 马松华 《中国安全科学学报》 EI CAS CSCD 北大核心 2026年第4期57-64,共8页
为预防超期服役压力管道失效,在故障树分析法(FTA)基础上,提出基于蝴蝶优化算法(BOA)与相似聚合法(SAM)相结合的方法,分析超期服役压力管道失效概率。首先,分析超期压力管道失效原因,建立失效故障树;然后,通过邀请专家评估故障树底事件... 为预防超期服役压力管道失效,在故障树分析法(FTA)基础上,提出基于蝴蝶优化算法(BOA)与相似聚合法(SAM)相结合的方法,分析超期服役压力管道失效概率。首先,分析超期压力管道失效原因,建立失效故障树;然后,通过邀请专家评估故障树底事件,并通过SAM方法和改进BOA-SAM聚合专家意见,计算对比2种方法的不确定性值来评估聚合的准确性(不确定性值越小,聚合结果越精确);最后,计算顶事件失效概率并进行重要度分析。结果表明:改进后的BOA-SAM方法所得聚合结果的不确定性值,均显著低于传统SAM方法,其中最明显的为底事件X6,其不确定性值降低7.6%,顶事件的失效概率为0.117641;应力腐蚀开裂、第三方破坏、焊缝开裂和介质中H2S含量偏高等为事故发生的关键因素。 展开更多
关键词 蝴蝶优化算法(BOA) 相似聚合法(SAM) 故障树分析法(FTA) 超期服役压力管道 风险评估 失效概率
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基于IBOA-RF的直流故障电弧诊断研究 认领 引用
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作者 罗希元 刘树鑫 +1 位作者 邢朝建 李艳凯 《电器与能效管理技术》 2026年第3期1-9,共9页
针对低压直流系统中不同工况下故障电弧特征微弱、难以准确识别的问题,提出一种基于改进蝴蝶优化算法随机森林(IBOA-RF)的直流电弧故障诊断方法。首先,采用鲸鱼优化算法(WOA)优化改进自适应噪声完备集合经验模态分解(ICEEMDAN)参数,并... 针对低压直流系统中不同工况下故障电弧特征微弱、难以准确识别的问题,提出一种基于改进蝴蝶优化算法随机森林(IBOA-RF)的直流电弧故障诊断方法。首先,采用鲸鱼优化算法(WOA)优化改进自适应噪声完备集合经验模态分解(ICEEMDAN)参数,并对电流信号进行分解得到多个本征模态函数(IMF);其次,筛选有效分量,并提取层次加权排列熵(HWPE)构建特征向量;最后,引入反向学习机制与动态边界调整策略对蝴蝶优化算法(BOA)进行改进,并建立IBOA-RF诊断模型进行故障识别。结果表明,该方法在多种典型工况下的平均识别准确率达97.92%,验证了该方法在直流电弧故障诊断领域的有效性。 展开更多
关键词 直流故障电弧 特征提取 层次加权排列熵 改进蝴蝶优化算法 故障诊断
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基于BOA-VMD-AWTD算法的TDLAS检测信号降噪方法研究 认领 引用 被引量：4
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作者 张伏 刘志华 +2 位作者 颜宝苹 王甲甲 付三玲 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2025年第10期2915-2921,共7页
为降低可调谐半导体激光吸收光谱技术中二次谐波信号中噪声对信号质量及浓度反演准确性的影响,本研究以重构信号与参考信号均方误差和相关性损失相结合构建优化目标函数,采用BOA优化VMD关键参数惩罚因子α、分解层数k、小波分解层数和... 为降低可调谐半导体激光吸收光谱技术中二次谐波信号中噪声对信号质量及浓度反演准确性的影响,本研究以重构信号与参考信号均方误差和相关性损失相结合构建优化目标函数,采用BOA优化VMD关键参数惩罚因子α、分解层数k、小波分解层数和阈值系数,获得最优参数组合,提高VMD信号分解准确性,结合本征模态函数能量分布和相关性指标,设计基于能量-相关性融合的评分机制,提升算法在不同信号特征下自适应能力。以CO气体在1567 nm处吸收光谱为例,选取EMD、VMD、BOA-VMD、PSO-VMD及BOA-VMD-AWTD五种降噪算法通过仿真对所提方法有效性验证,仿真结果表明,BOA-VMD-AWTD降噪算法表现最佳,SNR提升14.70 dB,NCC值达0.9993。分别采用PSO-VMD、BOA-VMD、BOA-VMD-AWTD对试验获得的二次谐波信号降噪处理,试验结果表明,0.01%~0.10%浓度范围内不同浓度CO二次谐波降噪后信号幅值线性拟合度R2达0.999。为验证BOA-VMD-AWTD算法稳定性,对预设体积浓度为0.05%CO连续采样,并对采集浓度数据稳定性分析,降噪后标准差σ为0.0005%,噪声得到有效抑制,降噪前后信号均值保持不变,为TDLAS信号处理提供有效技术支持。 展开更多
关键词 TDLAS 变分模态分解 小波阈值降噪 蝴蝶优化算法
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基于BOA-SVM的冷源系统温度传感器偏差故障检测 认领 引用 被引量：3
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作者 周璇 闫学成 +1 位作者 闫军威 梁列全 《控制理论与应用》 EI CAS CSCD 北大核心 2025年第5期921-930,共10页
针对当前因温度传感器偏差故障识别率低,严重影响冷源系统节能可靠运行的问题,提出一种基于贝叶斯优化支持向量机BOA-SVM组合优化算法的偏差故障检测方法.该方法融合了贝叶斯优化算法(BOA)和支持向量机(SVM)技术,适用于小样本、非线性... 针对当前因温度传感器偏差故障识别率低,严重影响冷源系统节能可靠运行的问题,提出一种基于贝叶斯优化支持向量机BOA-SVM组合优化算法的偏差故障检测方法.该方法融合了贝叶斯优化算法(BOA)和支持向量机(SVM)技术,适用于小样本、非线性故障数据,同时克服了SVM算法对核函数参数与惩罚因子强敏感性的问题.论文建立了广州市某办公建筑冷源系统Trnsys仿真模型,对室外干球、冷冻供水与冷却进水3种温度传感器不同程度的偏差故障进行模拟.仿真结果表明,与本文提出的其他方法相比,该方法准确率高,泛化能力及鲁棒性强,能够满足冷源系统温度传感器偏差故障的检测需求,保障空调系统的安全、高效与稳定运行. 展开更多
关键词 冷源系统 温度传感器 贝叶斯优化 支持向量机 故障检测 Trnsys
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基于BOA-BP神经网络的四旋翼飞行器路径优化 认领 引用 被引量：1
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作者 王舒玮 李嘉 +1 位作者 冯健 岳彩宾 《现代防御技术》 北大核心 2025年第3期74-81,共8页
针对四旋翼飞行器在多障碍物环境中飞行时容易出现路径规划不准确的问题,提出了基于蝴蝶算法(BOA)的BP神经网络优化方法。将四旋翼飞行器在设定路径中的所有途经点作为神经网络的训练样本,通过BOA-BP算法对神经网络进行训练,从而确定了... 针对四旋翼飞行器在多障碍物环境中飞行时容易出现路径规划不准确的问题,提出了基于蝴蝶算法(BOA)的BP神经网络优化方法。将四旋翼飞行器在设定路径中的所有途经点作为神经网络的训练样本,通过BOA-BP算法对神经网络进行训练,从而确定了最佳飞行路径。仿真结果表明,与传统的BOA算法相比,所提出的BOA-BP算法模型可以有效减小四旋翼飞行器路径的误差,均方根误差可从1.60%降低到0.003%。 展开更多
关键词 四旋翼 飞行器 蝴蝶优化算法 BP神经网络 路径优化 训练样本 误差处理
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结合不均衡样本生成及BOA-DRSN的扬声器异常声分类 认领 引用 被引量：1
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作者 周静雷 李振业 +1 位作者 路昌 李丽敏 《西安工程大学学报》 CAS 2025年第4期37-45,共9页
扬声器生产过程中,其正常数据与故障数据比例可能会严重失调,从而导致样本分布不均匀,进而影响故障诊断模型的准确率及可靠性。因此,文中根据样本生成扩增和优化深度学习网络的理念提出了一种新的扬声器异常声分类方法。首先,考虑到原... 扬声器生产过程中,其正常数据与故障数据比例可能会严重失调,从而导致样本分布不均匀,进而影响故障诊断模型的准确率及可靠性。因此,文中根据样本生成扩增和优化深度学习网络的理念提出了一种新的扬声器异常声分类方法。首先,考虑到原始数据特征过于复杂而导致生成样本的质量较差,对扬声器异常声响应信号进行变分模态分解(variational mode decomposition,VMD)突出原始样本的局部特征;其次,从扩增样本角度出发提升模型故障诊断精度,使用最小二乘生成对抗网络(least squares generative adversarial networks,LSGAN)进行对抗训练,生成具有真实样本特征的虚拟样本;最后,选用蝴蝶优化算法(butterfly optimization algorithm,BOA)在大规模权重空间中高效寻优以加速模型收敛,利用深度残差收缩网络(deep residual shrinkage network,DRSN)模型进行扬声器异常声分类,从而提升在样本不均衡情况下的分类准确率及诊断稳定性。实验结果表明:该方法能有效降低误判率,在样本不均衡情况下有效提高故障诊断准确率以及分类诊断的稳定性,其分类平均准确率可达0.9912。 展开更多
关键词 故障诊断 数据不均衡 异常声分类 深度残差收缩网络(DRSN) 蝴蝶优化算法(BOA) 最小二乘生成对抗网络(LSGAN)
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基于BOA-RVM特征优选和Prophet-LSTM的锅炉受热面壁温预测 认领 引用 被引量：1
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作者 麻淑芳 王秀慧 张晗 《锅炉技术》 北大核心 2025年第4期10-17,共8页
及时准确地对锅炉受热面壁温进行预测对于保证电厂的安全稳定运行具有重要意义。提出一种蝴蝶优化算法-相关向量机(BOA-RVM)和Prophet-长短时记忆神经网络(LSTM)相结合的锅炉受热面壁温预测组合模型。利用RVM筛选出与壁温相关性最高的... 及时准确地对锅炉受热面壁温进行预测对于保证电厂的安全稳定运行具有重要意义。提出一种蝴蝶优化算法-相关向量机(BOA-RVM)和Prophet-长短时记忆神经网络(LSTM)相结合的锅炉受热面壁温预测组合模型。利用RVM筛选出与壁温相关性最高的重要特征参数集合,降低后续预测模型的复杂度和运算量。针对RVM核参数选择难题,利用BOA对其进行全局寻优;利用Prophet模型对壁温数据进行自适应分解,将其分解为结构简单、波形平滑的趋势项、周期项和波动项,并分别建立LSTM模型进行预测。将预测结果综合叠加得到原始壁温数据的预测结果。基于实际锅炉运行数据开展试验,结果表明:所提方法预测结果的平均相对误差和均方根误差指标分别为0.15和1.06,相对于对比方法分别提升超过8.59%和9.22%。 展开更多
关键词 壁温预测 特征选择 长短时记忆神经网络 蝴蝶优化算法 参数寻优
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A Hybrid Moth Flame Optimization Algorithm for Global Optimization 认领 引用 被引量：9
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作者 Saroj Kumar Sahoo Apu Kumar Saha 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第5期1522-1543,共22页
The Moth Flame Optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization problems.However,it still suffers from obt... The Moth Flame Optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization problems.However,it still suffers from obtaining quality solution and slow convergence speed.On the other hand,the Butterfly Optimization Algorithm(BOA)is a comparatively new algorithm which is gaining its popularity due to its simplicity,but it also suffers from poor exploitation ability.In this study,a novel hybrid algorithm,h-MFOBOA,is introduced,which integrates BOA with the MFO algorithm to overcome the shortcomings of both the algorithms and at the same time inherit their advantages.For performance evaluation,the proposed h-MFOBOA algorithm is applied on 23 classical benchmark functions with varied complexity.The tested results of the proposed algorithm are compared with some well-known traditional meta-heuristic algorithms as well as MFO variants.Friedman rank test and Wilcoxon signed rank test are employed to measure the performance of the newly introduced algorithm statistically.The computational complexity has been measured.Moreover,the proposed algorithm has been applied to solve one constrained and one unconstrained real-life problems to examine its problem-solving capability of both type of problems.The comparison results of benchmark functions,statistical analysis,real-world problems confirm that the proposed h-MFOBOA algorithm provides superior results compared to the other conventional optimization algorithms. 展开更多
关键词 Moth fame optimization algorithm Butterfly optimization algorithm Bio-inspired Benchmark functions Friedman rank test
基于HPSOBOA的永磁/磁阻双转子电机无传感器控制 认领 引用 被引量：2
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作者 孔晓光 柯灿灿 王伟凡 《工业仪表与自动化装置》 2025年第2期3-8,36,共6页
为了实现对永磁/磁阻双转子电机(PM/R-DRM)的高精度无传感器控制,提出了一种基于新型滑模面函数和混合控制率的滑模观测器(SMO)。根据Lyapunov稳定性判据,证明了该方法的稳定性。为了避免反复调节SMO参数和转速环PI,设计了一种混合粒子... 为了实现对永磁/磁阻双转子电机(PM/R-DRM)的高精度无传感器控制,提出了一种基于新型滑模面函数和混合控制率的滑模观测器(SMO)。根据Lyapunov稳定性判据,证明了该方法的稳定性。为了避免反复调节SMO参数和转速环PI,设计了一种混合粒子群蝴蝶算法(HPSOBOA)用于系统的参数整定,并通过实验验证了这种融合机制能够改善BOA收敛速度慢和PSO易陷入局部最优的问题。使用Matlab/Simulink搭建模型,对比仿真结果可以看出,新型SMO明显削弱了传统SMO的固有抖振,并有效减小了转子位置的估计误差。 展开更多
关键词 双转子电机 滑模观测器 粒子群算法 蝴蝶算法
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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model 认领 引用 被引量：2
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作者 Tamilvizhi Thanarajan Youseef Alotaibi +1 位作者 Surendran Rajendran Krishnaraj Nagappan 《Computers, Materials & Continua》 SCIE EI 2023年第8期1995-2013,共19页
Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communi... Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches. 展开更多
关键词 Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis
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基于BOA-RF的熔融沉积成型翘曲变形量预测方法 认领 引用
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作者 周昊飞 万家豪 张晨怡 《塑料工业》 CAS CSCD 北大核心 2025年第8期108-114,共7页
为提升熔融沉积成型(FDM)翘曲变形量预测性能,提出了基于蝴蝶优化随机森林的FDM成型翘曲变形量预测方法。首先,利用全因子实验分析找出影响制品翘曲的显著性过程参数,作为预测模型的输入变量;而后,以均方误差作为适应度值,采用蝴蝶优化... 为提升熔融沉积成型(FDM)翘曲变形量预测性能,提出了基于蝴蝶优化随机森林的FDM成型翘曲变形量预测方法。首先,利用全因子实验分析找出影响制品翘曲的显著性过程参数,作为预测模型的输入变量;而后,以均方误差作为适应度值,采用蝴蝶优化算法对随机森林中决策树数量、最大树深进行参数优化,利用优化后的随机森林构建适宜于少样本情况下的FDM成型翘曲变形量预测模型;最后,将所提预测模型分别与基于遗传算法优化BP神经网络、蚁群算法优化BP神经网络和自适应布谷鸟优化稀疏约束深度信念网络的预测模型进行预测性能对比。结果表明,在有限样本数量情况下所提预测模型的均方误差(MSE)为0.0036、运行100次的平均误差百分比波动范围为[1.52%,3.12%],均优于对比模型的预测结果,验证了所提模型具有更好的预测精度和泛化能力。 展开更多
关键词 随机森林 蝴蝶优化 熔融沉积成型 翘曲变形量 质量预测
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基于BOA‑VMD‑SVD的MEMS陀螺仪信号降噪方法研究 认领 引用
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作者 马星河 闫崇威 《武汉大学学报（工学版）》 CAS CSCD 北大核心 2025年第7期1130-1138,共9页
针对微机电系统(micro-electro-mechanical system,MEMS)加速度计输出信号中随机噪声较大的问题,提出一种基于蝴蝶优化算法(butterfly optimization algorithm,BOA)的变分模态分解(variational mode decomposition,VMD)联合奇异值分解(s... 针对微机电系统(micro-electro-mechanical system,MEMS)加速度计输出信号中随机噪声较大的问题,提出一种基于蝴蝶优化算法(butterfly optimization algorithm,BOA)的变分模态分解(variational mode decomposition,VMD)联合奇异值分解(singular value decomposition,SVD)的随机噪声降噪方法。首先应用BOA-VMD算法将加速度计信号分解为K个最优的IMF(intrinsic mode function)分量;其次计算分解后的各IMF分量的排列熵值,并将其划分为加速度计信号主导的IMF分量、噪声主导的IMF分量以及噪声信号3种类型;再对噪声主导的IMF分量进行SVD分解降噪,舍弃噪声分量;最后将加速度计信号主导分量与降噪后的噪声主导分量进行重构,得到最终信号。仿真与实验数据表明:相较于VMD联合小波阈值方法,BOA-VMD-SVD算法的信噪比提高了19.8%,均方根误差下降了44.5%;相较于VMD-SVD算法,BOA-VMD-SVD算法的信噪比提高了15.6%,均方根误差下降了19.5%。这表明所提算法在处理MEMS加速度计信号中的随机噪声时具有更好的去噪效果,进而证明了所提方法的有效性。 展开更多
关键词 微机电系统 蝴蝶优化算法 奇异值分解 随机噪声 去噪
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Air Combat Assignment Problem Based on Bayesian Optimization Algorithm 认领 引用 被引量：3
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作者 FU LI LONG XI HE WENBIN 《Journal of Shanghai Jiaotong university(Science)》 EI 2022年第6期799-805,共7页
In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss ... In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss the number of fighters on both sides,and apply cluster analysis to divide our fighter into the same number of groups as the enemy.On this basis,we sort each of our fighters'different advantages to the enemy fighters,and obtain a series of target allocation schemes for enemy attacks by first in first serviced criteria.Finally,the maximum advantage function is used as the target,and the BOA is used to optimize the model.The simulation results show that the established model has certain decision-making ability,and the BOA can converge to the global optimal solution at a faster speed,which can effectively solve the air combat task assignment problem. 展开更多
关键词 air combat task assignment first in first serviced criteria Bayesian optimization algorithm(BOA)
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