The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is crit...The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.展开更多
针对电动汽车单一电源电流冲击大的问题,构建了电容半主动拓扑的复合电源,并提出一种基于小波变换与遗传算法(genetic algorithm,GA)优化模糊控制的能量管理策略。利用Haar小波分解需求功率,以高频分量及储能元件荷电状态(state of char...针对电动汽车单一电源电流冲击大的问题,构建了电容半主动拓扑的复合电源,并提出一种基于小波变换与遗传算法(genetic algorithm,GA)优化模糊控制的能量管理策略。利用Haar小波分解需求功率,以高频分量及储能元件荷电状态(state of charge,SOC)为输入,通过GA优化隶属度函数参数实现功率精准分配。在新欧洲驾驶循环(new European driving cycle,NEDC)及中国轻型汽车行驶工况-乘用车(China light-duty vehicle test cycle-passenger car,CLTC-P)等工况下的仿真表明,相比单一电源策略,所提策略峰值电流平均降低约28.0%,均方根(root mean square,RMS)电流平均降低约21.0%,并将高电流区间占比压缩至6%以内,电池温升幅度降低34.8%和37.8%;在不同SOC条件下均表现出鲁棒性,对电流指标的优化幅度稳定保持在20%~28%区间,有效延长了电池循环寿命。展开更多
针对电力电子化配电网谐波源随机波动引发的治理效率与成本问题,提出融合谐波源随机表征与多目标粒子群-遗传算法(multi-objective particle swarm optimization-genetic algorithm,MOPSO-GA)的并联有源电力滤波器(shunt active power f...针对电力电子化配电网谐波源随机波动引发的治理效率与成本问题,提出融合谐波源随机表征与多目标粒子群-遗传算法(multi-objective particle swarm optimization-genetic algorithm,MOPSO-GA)的并联有源电力滤波器(shunt active power filter,SAPF)优化配置策略。基于中心极限定理,采用正态分布与均匀分布构建谐波幅值与相位的概率模型,结合MOPSO-GA算法实现多目标优化。仿真结果表明,在IEEE 18节点系统中仅配置3台SAPF即可将总谐波畸变率从19.88%降至3.32%,电压偏差从10.4%控制至4.5%,SAPF较传统MOPSO算法减少1台,总容量更经济,算法收敛速度与帕累托前沿分布性显著提升。并进一步通过RT-Lab半实物实验平台验证,在真实谐波源下将关键节点谐波电压畸变率从25.24%降至2.12%,该策略为复杂配电网谐波治理提供高效经济的解决方案。展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified...Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.展开更多
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result...In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.展开更多
Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it...Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.展开更多
In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwa...In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.展开更多
文摘The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.
文摘针对电动汽车单一电源电流冲击大的问题,构建了电容半主动拓扑的复合电源,并提出一种基于小波变换与遗传算法(genetic algorithm,GA)优化模糊控制的能量管理策略。利用Haar小波分解需求功率,以高频分量及储能元件荷电状态(state of charge,SOC)为输入,通过GA优化隶属度函数参数实现功率精准分配。在新欧洲驾驶循环(new European driving cycle,NEDC)及中国轻型汽车行驶工况-乘用车(China light-duty vehicle test cycle-passenger car,CLTC-P)等工况下的仿真表明,相比单一电源策略,所提策略峰值电流平均降低约28.0%,均方根(root mean square,RMS)电流平均降低约21.0%,并将高电流区间占比压缩至6%以内,电池温升幅度降低34.8%和37.8%;在不同SOC条件下均表现出鲁棒性,对电流指标的优化幅度稳定保持在20%~28%区间,有效延长了电池循环寿命。
文摘针对电力电子化配电网谐波源随机波动引发的治理效率与成本问题,提出融合谐波源随机表征与多目标粒子群-遗传算法(multi-objective particle swarm optimization-genetic algorithm,MOPSO-GA)的并联有源电力滤波器(shunt active power filter,SAPF)优化配置策略。基于中心极限定理,采用正态分布与均匀分布构建谐波幅值与相位的概率模型,结合MOPSO-GA算法实现多目标优化。仿真结果表明,在IEEE 18节点系统中仅配置3台SAPF即可将总谐波畸变率从19.88%降至3.32%,电压偏差从10.4%控制至4.5%,SAPF较传统MOPSO算法减少1台,总容量更经济,算法收敛速度与帕累托前沿分布性显著提升。并进一步通过RT-Lab半实物实验平台验证,在真实谐波源下将关键节点谐波电压畸变率从25.24%降至2.12%,该策略为复杂配电网谐波治理提供高效经济的解决方案。
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
文摘Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312200) and the Center for Bioinformatics Pro-gram Grant of Harvard Center of Neurodegeneration and Repair,Harvard Medical School, Harvard University, Boston, USA
文摘In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.
基金Supported by School of Engineering, Napier University, United Kingdom, and partially supported by the National Natural Science Foundation of China (No.60273093).
文摘Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.
文摘In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.
