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The Optimization Design of Particle-Reinforced Composite Materials Based on the Artificial Fish Swarm Algorithm and Voronoi Cell Finite Element Method 认领 引用
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作者 Peng Zhang Ran Guo 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第3期316-328,共13页
In this study,a collaborative optimization framework combining artificial fish swarm algorithm(AFSA)and Voronoi cell finite element method(VCFEM)is proposed to solve the problem of the influence of microstructure dist... In this study,a collaborative optimization framework combining artificial fish swarm algorithm(AFSA)and Voronoi cell finite element method(VCFEM)is proposed to solve the problem of the influence of microstructure distribution on the macroscopic mechanical properties of particle-reinforced composites.Compared with the traditional displacement-based finite element method,VCFEM significantly improves the computational efficiency of multi-inclusion problems by means of generalized stress function and element local adaptation technology,and accurately captures the stress field discontinuity and interface stress concentration phenomenon.At the same time,AFSA abandons the dependence of traditional optimization methods on simplified models,and directly searches for the global optimal solution through discrete topological variables(particle positions).Its natural selection mechanism and swarm intelligence characteristics effectively overcome the local optimal trap.The numerical simulation results show that the proposed method exhibits high accuracy in both single-and multi-inclusion models(the error with the commercial software MARC is less than 5%),and in the complex model with 100 inclusions,the maximum Mises stress is reduced by 32.6%after optimization.The synergistic effect of the two breaks through the trade-off between efficiency and accuracy in traditional optimization,and provides the ability of both computational efficiency and global optimization for multi-scale composite material design. 展开更多
关键词 Particle-reinforced composites Artificial fish swarm algorithm Voronoi cell finite element method Optimization algorithms
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Unmanned wave glider heading model identification and control by artificial fish swarm algorithm 认领 引用 被引量：3
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作者 WANG Lei-feng LIAO Yu-lei +2 位作者 LI Ye ZHANG Wei-xin PAN Kai-wen 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第9期2131-2142,共12页
We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,th... We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified. 展开更多
关键词 unmanned wave glider artificial fish swarm algorithm heading model parameters identification control parameters optimization
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Development of an Artificial Fish Swarm Algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background 认领 引用
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作者 Sheng Bai Feng Bao +1 位作者 Fengzhi Zhao Miaomiao Liu 《Fluid Dynamics & Materials Processing》 EI 2020年第5期935-946,共12页
The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor net... The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic background.The nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual environment.The results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level congestion.We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent. 展开更多
关键词 Artificial fish swarm algorithm wireless sensor network network measurement hydrodynamics
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Intelligent approach of score-based artificial fish swarm algorithm(SAFSA)for Parkinson’s disease diagnosis 认领 引用 被引量：1
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作者 Syed Haroon Abdul Gafoor Padma Theagarajan 《International Journal of Intelligent Computing and Cybernetics》 EI 2022年第4期540-561,共22页
Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resu... Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient. 展开更多
关键词 Parkinson disease dysphonia features Feature subset selection Score-based artificial fish swarm algorithm(SAFSA) Singular value decomposition(SVD) Classification
Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks 认领 引用
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作者 Ganeshan Keerthana Panneerselvam Anandan Nandhagopal Nachimuthu 《Computers, Materials & Continua》 SCIE EI 2021年第1期903-917,共15页
Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectiv... Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectively.But they are susceptible to malicious attacks,which mainly targets particular significant nodes.Therefore,the robustness of the network becomes important for ensuring the network security.This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization(RHAFS-SA)Algorithm.It is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree distribution.The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm(IAFSA)by the simulated annealing(SA)algorithm.The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate.For experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks.The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects. 展开更多
关键词 Free scale networks robustness malicious attacks fish swarm algorithm
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Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach 认领 引用 被引量：2
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作者 Saud S.Alotaibi Eatedal Alabdulkreem +5 位作者 Sami Althahabi Manar Ahmed Hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期737-751,共15页
Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the patte... Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions. 展开更多
关键词 Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning
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Approach to WTA in air combat using IAFSA-IHS algorithm 认领 引用 被引量：13
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作者 LI Zhanwu CHANG Yizhe +3 位作者 KOU Yingxin YANG Haiyan XU An LI You 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第3期519-529,共11页
In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ... In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem. 展开更多
关键词 air combat weapon target assignment improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） artificial fish swarm algorithm（AFSA） harmony search（HS）
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Research on Flight First Service Model and Algorithms for the Gate Assignment Problem 认领 引用 被引量：4
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作者 Jiarui Zhang Gang Wang Siyuan Tong 《Computers, Materials & Continua》 SCIE EI 2019年第9期1091-1104,共14页
Aiming at the problem of gate allocation of transit flights,a flight first service model is established.Under the constraints of maximizing the utilization rate of gates and minimizing the transit time,the idea of“fi... Aiming at the problem of gate allocation of transit flights,a flight first service model is established.Under the constraints of maximizing the utilization rate of gates and minimizing the transit time,the idea of“first flight serving first”is used to allocate the first time,and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal solution.That means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly,and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal local region.The experimental data show that the maximum utilization of the gate is 27.81%higher than that of the“first come first serve”method when the apron is not limited,and the hybrid algorithm has fewer iterations than the simulated annealing algorithm alone,with the overall passenger transfer tension reducing by 1.615;the hybrid algorithm has faster convergence and better performance than the artificial fish swarm algorithm alone.The experimental results indicate that the hybrid algorithm of fish swarm and simulated annealing can achieve higher utilization rate of gates and lower passenger transfer tension under the idea of“first flight serving first”. 展开更多
关键词 Gate assignment flight first service model fish swarm algorithm passenger transfer tension
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改进RRT-Connect与AFSA融合算法移动机器人路径规划 认领 引用 被引量：1
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作者 陈志澜 古春祥 《山东大学学报（工学版）》 CAS CSCD 北大核心 2026年第3期73-83,共11页
针对RRT-Connect算法在路径规划中搜索效率低、目标导向性弱、路径冗余节点多、平滑性不佳的问题,在改进RRT-Connect算法与人工鱼群算法基础上,提出ARRT-Connect融合算法.该算法引入中间节点,采用目标偏置策略、引力势场引导、自适应步... 针对RRT-Connect算法在路径规划中搜索效率低、目标导向性弱、路径冗余节点多、平滑性不佳的问题,在改进RRT-Connect算法与人工鱼群算法基础上,提出ARRT-Connect融合算法.该算法引入中间节点,采用目标偏置策略、引力势场引导、自适应步长调节及剪枝优化,并结合B样条曲线平滑路径;改进人工鱼群算法步长与视野范围,增强全局搜索能力.试验表明,与RRT-Connect算法相比,ARRT-Connect融合算法在简单和复杂环境中平均耗时分别减少82.22%和76.92%,平均路径长度分别缩短17.41%和19.38%,平均节点数分别减少79.21%和77.84%.将其应用于现实场景,移动机器人路径长度和耗时明显缩短,路径转折更平缓,验证了该算法有效性、优越性与可行性. 展开更多
关键词 融合算法 移动机器人 路径规划 RRT-Connect 人工鱼群算法
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基于改进人工鱼群粒子滤波算法的车辆状态估计 认领 引用
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作者 刘文光 蒋祝安 +2 位作者 何仁 丁贝 车华军 《江苏大学学报（自然科学版）》 CAS 北大核心 2026年第3期267-274,291,共8页
针对粒子滤波(PF)对车辆状态估计中出现的粒子权重退化导致估计精度下降甚至发散的问题,提出了一种改进的人工鱼群粒子滤波(AFSA-PF)车辆状态估计方法.首先,为提高AFSA全局搜索能力和减少陷入局部极值的风险,引入衰减函数动态调整感知视... 针对粒子滤波(PF)对车辆状态估计中出现的粒子权重退化导致估计精度下降甚至发散的问题,提出了一种改进的人工鱼群粒子滤波(AFSA-PF)车辆状态估计方法.首先,为提高AFSA全局搜索能力和减少陷入局部极值的风险,引入衰减函数动态调整感知视野,在迭代前期、后期分别采用较大、较小感知视野进行全局搜索和局部搜索;其次,改变人工鱼运动中的随机步长策略,采用自适应步长实现在不同情境下大、小步长的动态切换;最后,利用上述改进后AFSA的觅食和聚群行为,优化PF状态估计中的粒子权重计算与粒子数据重采样过程.采用Carsim与Simulink联合仿真试验进行验证,结果表明:相比于AFSA-PF,改进的AFSA-PF在双移线工况下,车辆横摆角速度估计值的平均绝对误差(MAE)、均方根误差(RMSE)分别减少了40.1%、34.9%,车辆质心侧偏角估计值的MAE、RMSE分别减少35.1%、33.5%;阶跃工况下,横摆角速度估计值的MAE、RMSE分别减少52.7%、36.3%,质心侧偏角估计值的MAE、RMSE分别减少51.5%、24.0%. 展开更多
关键词 智能汽车控制系统 车辆状态估计 粒子滤波 人工鱼群算法 衰减函数 自适应步长
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基于人工鱼群算法的智能微电网分布式电压分层控制研究 认领 引用
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作者 侯伟 宋承继 +1 位作者 乔薇娜 刘强锋 《自动化仪表》 CAS 2026年第8期35-39,44,共5页
由于缺少对分布式电源无功裕度属性约束条件的分析,控制后系统电压偏差率较高、控制效果不理想。为此,提出基于人工鱼群算法的智能微电网分布式电压分层控制方法。智能微电网会产生电压波动。基于智能微电网电压波动的形成机理,融合分... 由于缺少对分布式电源无功裕度属性约束条件的分析,控制后系统电压偏差率较高、控制效果不理想。为此,提出基于人工鱼群算法的智能微电网分布式电压分层控制方法。智能微电网会产生电压波动。基于智能微电网电压波动的形成机理,融合分布式电源动态模型,利用对称分量法解析系统输出电压的正序分量与负序分量。基于计算结果,进一步求取系统电压不平衡补偿参量。根据电压跟踪指令,对时间的预测周期进行优化。同时,挖掘分布式电源无功裕度属性的约束条件。基于此,将系统分布式电压划分为两个层区,并以电压浮动值最小与线损最小为目标建立电压控制模型。采用人工鱼群算法求解模型,以获取电压控制因子。通过拟合计算两个层区的电压控制因子得到电压修正值,以实现电压分层控制。结合对比试验,利用所提方法对系统分布式电压进行分层控制后,得到的电压偏差率较低、控制效果较好。该研究为大规模分布式光伏并网引起的无功电压波动和不平衡问题提供了有效的解决方案。 展开更多
关键词 智能微电网 人工鱼群算法 分布式电压 分层控制 无功裕度 电压偏差
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面向复杂约束问题的大模型辅助人工鱼群优化算法研究 认领 引用
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作者 熊永平 张立臣 卢爱芬 《湖南邮电职业技术学院学报》 2026年第1期41-45,共5页
为了解决复杂约束下群智能算法的局部最优和可行域断裂问题,研究提出一种基于大模型辅助的人工鱼群算法新框架LLM-AFSA,其采用一种双层协同优化体系,上层为基于大语言模型的宏观决策器,下层为具体搜索的人工鱼群演化引擎。通过大模型的... 为了解决复杂约束下群智能算法的局部最优和可行域断裂问题,研究提出一种基于大模型辅助的人工鱼群算法新框架LLM-AFSA,其采用一种双层协同优化体系,上层为基于大语言模型的宏观决策器,下层为具体搜索的人工鱼群演化引擎。通过大模型的语义引导,实时优化群体搜索策略,克服了传统方法的局限性。实验结果表明,LLM-AFSA算法在多个高维、复杂约束问题上表现出较传统算法更为显著的收敛精度和效率优势,特别是在解决非连续可行域和高约束冲突死区问题时,展现了强大的鲁棒性和全局最优求解能力,为复杂约束优化问题提供了一种可解释、强自适应的新型求解范式。 展开更多
关键词 人工鱼群算法 大模型辅助 复杂约束优化 双层协同优化 语义引导
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考虑碳捕集和电池梯次利用的混合发电厂调度优化 认领 引用
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作者 朱伟星 梅韵怡 +1 位作者 郭超 杨欢红 《电气自动化》 2026年第3期87-92,96,共6页
将退役铅酸电池用作电力储能,是提高资源利用率和平抑风光出力波动的有效措施。为推动退役铅酸电池再利用,同时缓解系统中碳捕集机组的高能耗问题,选用风-光-火-储一体化混合电厂为研究对象,提出了一种考虑光热辅助碳捕集(solar-thermal... 将退役铅酸电池用作电力储能,是提高资源利用率和平抑风光出力波动的有效措施。为推动退役铅酸电池再利用,同时缓解系统中碳捕集机组的高能耗问题,选用风-光-火-储一体化混合电厂为研究对象,提出了一种考虑光热辅助碳捕集(solar-thermal assisted carbon capture,SACC)和蓄电池梯次利用的调度策略。首先介绍了SACC单元;然后将其与考虑蓄电池梯次利用的混合发电厂相结合,将余热锅炉产生的蒸汽作为SACC单元解吸CO2的部分热源,增加了热能利用形式;其次,通过拉丁超立方抽样和改进K-means聚类提取系统典型运行场景,来减小风、光出力及负荷需求的不确定性引起的系统非经济运行问题;在典型场景基础上,以最大化系统收益为目标,运用改进的人工鱼群算法进行求解。最后,通过设置不同的运行策略进行对比。仿真结果表明所提方案降低了储能成本、丰富了热能的利用形式,在增加系统收益的同时减少了污染物的排放。 展开更多
关键词 光热辅助碳捕集 电池梯次利用 拉丁超立方抽样 K-means聚类 改进人工鱼群算法
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基于改进人工鱼群-粒子群算法的梯级水库群多目标优化调度算法 认领 引用
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作者 张侃侃 赵海峰 王兆才 《水利水电科技进展》 CSCD 北大核心 2026年第2期38-45,共8页
为解决梯级水库群优化调度中高维度、非线性的复杂优化问题,提出了一种两阶段多目标改进人工鱼群-粒子群(TMIAFS-PSO)算法。该算法采用分段映射扩展初始种群的搜索空间,通过调整自适应步长和引入多样化移动策略来增强局部和全局搜索能力... 为解决梯级水库群优化调度中高维度、非线性的复杂优化问题,提出了一种两阶段多目标改进人工鱼群-粒子群(TMIAFS-PSO)算法。该算法采用分段映射扩展初始种群的搜索空间,通过调整自适应步长和引入多样化移动策略来增强局部和全局搜索能力;采用两阶段过滤策略,保留符合约束条件的粒子,并加入改进人工鱼群优化策略,进一步扩大粒子搜索范围。金沙江下游的乌东德、白鹤滩、溪洛渡和向家坝梯级水库群实例验证结果表明,相较于其他算法,TMIAFS-PSO算法的帕累托解集表现出更好的收敛性和均匀性,体现了该算法的优越性,并通过分析TMIAFS-PSO算法所生成调度方案的水位变化,总结出该梯级水库群相对稳定的优化调度方案。 展开更多
关键词 梯级水库群优化调度 改进人工鱼群-粒子群算法 帕累托解集 多目标优化算法
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结合人工鱼群和RRT算法的机械臂路径规划 认领 引用 被引量：6
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作者 田玉冬 徐传征 《机械科学与技术》 CSCD 北大核心 2026年第1期44-56,共13页
为了解决快速搜索随机树(Rapid-exploration random tree,RRT)算法在高精度机械臂的路径规划中存在的问题,如采样点随机性强、路径指向性差、路径平滑度低、路径长等,提出了一种融合的人工鱼群算法(RRT-ASFA)来优化RRT生成的路径。首先,... 为了解决快速搜索随机树(Rapid-exploration random tree,RRT)算法在高精度机械臂的路径规划中存在的问题,如采样点随机性强、路径指向性差、路径平滑度低、路径长等,提出了一种融合的人工鱼群算法(RRT-ASFA)来优化RRT生成的路径。首先,为RRT提出了一个目标偏置策略,以减少采样点的随机性并优化目标方向;提出了步长自适应和搜索区域限制,以优化路径规划时间。其次,对于人工鱼群算法(Artificial fish swarming algorithm,ASFA),提出了自适应步长和自适应视场范围以使人工鱼群更快收敛;对RRT规划的路径的转折点进行了优化,使路径更短。最后,通过Hermite样条函数对路径进行了平滑处理。通过仿真实验发现,与传统的RRT算法、目标偏置RRT算法和RRT*算法相比,结合算法规划的路径长度更短,路径节点更少,这证明了该组合算法的可行性。 展开更多
关键词 RRT算法 人工鱼群算法 机械臂 路径规划
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SVC Video Transmission Optimization Algorithm in Software Defined Network 认领 引用
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作者 Zhe Liu 《China Communications》 SCIE CSCD 2018年第10期143-149,共7页
Scalable video coding(SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Softwar... Scalable video coding(SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Software Defined Network(SDN), most of the them are focused either on the number of transmission layers or on the optimization of transmission path for specific layer. In this paper, we propose a noval optimization algorithm for SVC to dynamically adjust the number of layers and optimize the transmission paths simultaneously. We establish the problem model based on the 0/1 knapsack model, and then solve it with Artificial Fish Swarm Algorithm. Additionally, the simulations are carried out on the Mininet platform, which show that our approach can dynamically adjust the number of layers and select the optimal paths at the same time. As a result, it can achieve an effective allocation of network resources which mitigates the congestion and reduces the loss of non-SVC stream. 展开更多
关键词 SVC SDN OpenFlow Mininet Artificial Fish Swarm Algorithm （AFSA） 0/1 knapsack model
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基于改进人工鱼群算法的AUV路径规划 认领 引用 被引量：1
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作者 郭阳 石博博 +4 位作者 衣正尧 朱嘉晟 刘志恒 曹雏清 赵立军 《舰船科学技术》 北大核心 2026年第6期141-149,共9页
针对水下无人艇(Autonomous Underwater Vehicle,AUV)路径规划问题,在传统人工鱼群算法的基础上提出一种融合差分进化算法的DE-IAFSA算法。通过引入自适应因子更新鱼群的视野范围和移动步长,提高算法的前期收敛速度与后期收敛精度;利用... 针对水下无人艇(Autonomous Underwater Vehicle,AUV)路径规划问题,在传统人工鱼群算法的基础上提出一种融合差分进化算法的DE-IAFSA算法。通过引入自适应因子更新鱼群的视野范围和移动步长,提高算法的前期收敛速度与后期收敛精度;利用对数函数优化个体表现,增强算法的适应性;融合差分进化算法的变异和交叉操作,以避免陷入局部最优。利用Matlab软件开展仿真实验,将改进后的DE-IAFSA算法与传统的人工鱼群算法进行对比。实验结果表明,改进后的DE-IAFSA算法比传统人工鱼群算法在三维地图(a)、地图(b)和地图(c)中收敛精度分别提高了18.2%、3.4%、2.6%,平均路径长度分别减少了12.9%、29.7%、19.8%,收敛速度分别提高了93%、82%、70%,进而验证了改进后算法的优化能力。该算法具有收敛速度快、收敛精度高、适应性强、避免陷入局部最优的特点。 展开更多
关键词 水下无人艇 路径规划 自适应因子 人工鱼群算法 差分进化算法
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基于EEMD-AFSA-CNN的混凝土坝变形预测模型 认领 引用 被引量：1
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作者 付思韬 赖宇杰 +1 位作者 顾冲时 顾昊 《水利水电科技进展》 CSCD 北大核心 2026年第1期48-53,共6页
为解决混凝土坝原型监测数据存在噪声干扰,用于变形预测的智能算法超参数众多且调优困难等问题,提出了基于集合经验模态分解(EEMD)-人工鱼群算法(AFSA)-卷积神经网络(CNN)的混凝土坝变形预测模型。该模型利用EEMD对原始变形数据进行分... 为解决混凝土坝原型监测数据存在噪声干扰,用于变形预测的智能算法超参数众多且调优困难等问题,提出了基于集合经验模态分解(EEMD)-人工鱼群算法(AFSA)-卷积神经网络(CNN)的混凝土坝变形预测模型。该模型利用EEMD对原始变形数据进行分解获取本征模态函数(IMF),采用小波阈值去噪方法对含噪IMF分量进行去噪处理并对各分量进行重构,并基于AFSA优化CNN模型的超参数,将重构后的数据用参数寻优后的CNN模型进行训练,并将训练好的模型用于预测。某特高拱坝实例验证结果表明,与CNN、极限学习机(ELM)、反向传播(BP)神经网络等模型进行对比,该模型在混凝土坝变形预测中具有更高的精度和更强的稳定性。 展开更多
关键词 混凝土坝变形预测 集合经验模态分解 人工鱼群算法 卷积神经网络 小波阈值去噪
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天然气全生命周期产量预测关键技术 认领 引用
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作者 王欣 吴晓茹 +5 位作者 张翀 邓力珲 贾虎 李杰 王军 于林 《工程科学学报》 EI CAS CSCD 北大核心 2026年第6期1379-1393,共15页
准确预测天然气井产量对开发决策优化具有重要意义.现有预测方法大多侧重整体建模,难以适应天然气井“增产—稳产—减产”的生命周期演变特征;且现有数据驱动模型大多忽略储层渗流场随时间演化的物理规律,难以准确反映物性的时序变化.... 准确预测天然气井产量对开发决策优化具有重要意义.现有预测方法大多侧重整体建模,难以适应天然气井“增产—稳产—减产”的生命周期演变特征;且现有数据驱动模型大多忽略储层渗流场随时间演化的物理规律,难以准确反映物性的时序变化.本文从生命周期视角出发,提出气井产量预测方法(Full life cycle gas production forecast model,FGPM).首先,通过断点检测算法划分气井生命周期,结合产量相对波动率判别气井生产阶段;随后,构建基于编码-解码结构的预测模型,针对生产阶段进行特征匹配训练,并集成为全生命周期模型;最后,从模型超参数优化和渗流规律融合两方面展开模型优化.实验证明:(1)全生命周期模型相较于单一周期模型预测精度更高.与LSTM、GRU、TCN和TimeMixer等方法对比,FGPM的预测精度分别提升了28.47%、26.55%、29.63%和41.20%.(2)面向FGPM设计的优化措施对模型性能提升起正向作用:(a)优化超参数后的模型,其百分比误差(MAPE)、平均绝对误差(MAE)和均方根误差(RMSE)分别降低了9.9%、16.2%和19.4%;(b)融合渗流规律约束的FGPM,其MAPE仅为4.874479%,模型性能得到进一步提升. 展开更多
关键词 天然气产量全生命周期划分 Pruned exact linear time(PELT)算法 编解码模型 人工鱼群算法 渗流规律约束 产量预测
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考虑多目标功率分配的储能能量管理策略研究 认领 引用
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作者 尹翔 余丹 +4 位作者 张效俊 崔福海 何蕾 夏远德 王哲 《高压电器》 CAS CSCD 北大核心 2026年第6期228-236,共9页
为了提高储能换电柜管理策略效率,促进其高效利用,提出了一个基于人工鱼群算法的多目标功率分配能量管理策略。首先,建立了换电柜系统模型,其次,建立了考虑电池电量、储能换电柜运行成本的多目标功率分配数学模型,然后运用具有收敛速度... 为了提高储能换电柜管理策略效率,促进其高效利用,提出了一个基于人工鱼群算法的多目标功率分配能量管理策略。首先,建立了换电柜系统模型,其次,建立了考虑电池电量、储能换电柜运行成本的多目标功率分配数学模型,然后运用具有收敛速度快、效率高等优点的人工鱼群算法求解该模型。通过仿真表明,该策略能够实现储能换电柜的功率分配,降低运行成本,提高运行效率。 展开更多
关键词 储能换电柜 功率分配策略 人工鱼群算法 多目标数学模型
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