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
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
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
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
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
针对启发式算法在无人机不规则复杂地形和多重威胁环境下进行三维航迹规划时,存在路径波动大和优化性能不足的问题,提出结合高程数据的凸包策略以及一种改进的樽海鞘群算法(ISSA)。首先,基于ASTER GDEMV3和Open Street Map数据,构建杭...针对启发式算法在无人机不规则复杂地形和多重威胁环境下进行三维航迹规划时,存在路径波动大和优化性能不足的问题,提出结合高程数据的凸包策略以及一种改进的樽海鞘群算法(ISSA)。首先,基于ASTER GDEMV3和Open Street Map数据,构建杭州某处山区和纽约城市区域的高程模型;其次,结合地形高程信息,采用凸包策略编码并通过B样条曲线构建路径;最后,对樽海鞘群算法在个体位置更新公式上加入自适应Alpha稳定分布策略与非线性扰动策略,以平衡算法的全局开发能力与局部探索能力,并引入贪婪策略和鱼类聚集装置策略,提高算法搜索效率和精度。利用CEC2020测试函数对所提算法进行实验对比,验证了改进算法的性能。实验结果表明,凸包策略能有效提升算法规划能力,且与传统算法相比,改进后的算法能够使无人机的寻优精度更高,代价函数更小。展开更多
为了提高人工鱼群算法AFSA(artificial fish swarm algorithm)的全局搜索能力及加快其收敛速度,提出一种将其与免疫算法IA(immune algorithm)进行结合的新方法,形成了免疫人工鱼群算法IAFSA(immuneartificial fish swarm algorithm),并...为了提高人工鱼群算法AFSA(artificial fish swarm algorithm)的全局搜索能力及加快其收敛速度,提出一种将其与免疫算法IA(immune algorithm)进行结合的新方法,形成了免疫人工鱼群算法IAFSA(immuneartificial fish swarm algorithm),并且利用该算法自动选取径向基函数RBF(radial basis function)神经网络中的输入变量,以及对网络中隐含层到输出层之间的权值进行训练,从而减少了RBF神经网络的工作量,提高了训练速度。用优化后的RBF神经网络进行短期负荷预测,结果表明,该方法具有较高的预测精度。展开更多
特征选择是网络入侵检测研究中的核心问题,为了提高网络入侵检测率,提出一种人工鱼群算法(AFSA)和支持向量机(SVM)相融合的网络入侵检测模型(AFSA-SVM)。将网络特征子集编码成人工鱼的位置,以5折交叉验证SVM训练模型检测率作为特征子集...特征选择是网络入侵检测研究中的核心问题,为了提高网络入侵检测率,提出一种人工鱼群算法(AFSA)和支持向量机(SVM)相融合的网络入侵检测模型(AFSA-SVM)。将网络特征子集编码成人工鱼的位置,以5折交叉验证SVM训练模型检测率作为特征子集优劣的评价标准,通过模拟鱼群的觅食、聚群及追尾行为找到最优特征子集,SVM根据最优特征子集进行网络入侵检测,并采用KDD CUP 99数据集进行仿真测试。仿真结果表明,相对于粒子群优化算法、遗传算法和原始特征法,AFSA-SVM提高了入侵检测效率和检测率,是一种有效的网络入侵检测模型。展开更多
基金Project(51779052)supported by the National Natural Science Foundation of ChinaProject(QC2016062)supported by the Natural Science Foundation of Heilongjiang Province,China+2 种基金Project(614221503091701)supported by the Research Fund from Science and Technology on Underwater Vehicle Laboratory,ChinaProject(LBH-Q17046)supported by the Heilongjiang Postdoctoral Funds for Scientific Research Initiation,ChinaProject(HEUCFP201741)supported by the Fundamental Research Funds for the Central Universities,China
文摘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.
文摘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.
基金financially supported by Natural Science Foundation of Heilongjiang Province of China[Grant No.LH2019F042].
文摘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.
基金supported by the National Natural Science Foundation of China(61472441)
文摘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.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43).
文摘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.
文摘针对启发式算法在无人机不规则复杂地形和多重威胁环境下进行三维航迹规划时,存在路径波动大和优化性能不足的问题,提出结合高程数据的凸包策略以及一种改进的樽海鞘群算法(ISSA)。首先,基于ASTER GDEMV3和Open Street Map数据,构建杭州某处山区和纽约城市区域的高程模型;其次,结合地形高程信息,采用凸包策略编码并通过B样条曲线构建路径;最后,对樽海鞘群算法在个体位置更新公式上加入自适应Alpha稳定分布策略与非线性扰动策略,以平衡算法的全局开发能力与局部探索能力,并引入贪婪策略和鱼类聚集装置策略,提高算法搜索效率和精度。利用CEC2020测试函数对所提算法进行实验对比,验证了改进算法的性能。实验结果表明,凸包策略能有效提升算法规划能力,且与传统算法相比,改进后的算法能够使无人机的寻优精度更高,代价函数更小。
文摘为了提高人工鱼群算法AFSA(artificial fish swarm algorithm)的全局搜索能力及加快其收敛速度,提出一种将其与免疫算法IA(immune algorithm)进行结合的新方法,形成了免疫人工鱼群算法IAFSA(immuneartificial fish swarm algorithm),并且利用该算法自动选取径向基函数RBF(radial basis function)神经网络中的输入变量,以及对网络中隐含层到输出层之间的权值进行训练,从而减少了RBF神经网络的工作量,提高了训练速度。用优化后的RBF神经网络进行短期负荷预测,结果表明,该方法具有较高的预测精度。
文摘特征选择是网络入侵检测研究中的核心问题,为了提高网络入侵检测率,提出一种人工鱼群算法(AFSA)和支持向量机(SVM)相融合的网络入侵检测模型(AFSA-SVM)。将网络特征子集编码成人工鱼的位置,以5折交叉验证SVM训练模型检测率作为特征子集优劣的评价标准,通过模拟鱼群的觅食、聚群及追尾行为找到最优特征子集,SVM根据最优特征子集进行网络入侵检测,并采用KDD CUP 99数据集进行仿真测试。仿真结果表明,相对于粒子群优化算法、遗传算法和原始特征法,AFSA-SVM提高了入侵检测效率和检测率,是一种有效的网络入侵检测模型。
