Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;"&g...Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper propose</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""> <span style="font-family:Verdana;">covariance</span><span style="font-family:Verdana;"> matrix learning differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally,</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is </span><span style="font-family:Verdana;">an effective algorithm</span><span style="font-family:Verdana;">.</span></span>展开更多
Multiphoton microscopy is the enabling tool for biomedical research,but the aberrations of biological tissues have limited its imaging performance.Adaptive optics(AO)has been developed to partially overcome aberration...Multiphoton microscopy is the enabling tool for biomedical research,but the aberrations of biological tissues have limited its imaging performance.Adaptive optics(AO)has been developed to partially overcome aberration to restore imaging performance.For indirect AO,algorithm is the key to its successful implementation.Here,based on the fact that indirect AO has an analogy to the black-box optimization problem,we successfully apply the covariance matrix adaptation evolution strategy(CMA-ES)used in the latter,to indirect AO in multiphoton microscopy(MPM).Compared with the traditional genetic algorithm(GA),our algorithm has a greater improvement in convergence speed and convergence accuracy,which provides the possibility of realizing real-time dynamic aberration correction for deep in vivo biological tissues.展开更多
在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learn...在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learning,CMA-DOL),旨在根据样本点的变化动态更新反向样本点的范围,提高样本多样性,防止陷入局部最优.本方法在分别由10、40、80个发电机组组成的3个测试系统上进行了验证,并与文献中的其他算法进行比较,对超过50次独立运行的结果进行统计度量,实验结果表明CMA-DOL可以获得更好的解决方案.展开更多
To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First...To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.展开更多
To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is prese...To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally,the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement(MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts,the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.展开更多
文摘Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper propose</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""> <span style="font-family:Verdana;">covariance</span><span style="font-family:Verdana;"> matrix learning differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally,</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is </span><span style="font-family:Verdana;">an effective algorithm</span><span style="font-family:Verdana;">.</span></span>
基金supported by the National Natural Science Foundation of China(Nos.62075135 and 61975126)the Science,Technology and Innovation Commission of Shenzhen Municipality(Nos.JCYJ20190808174819083 and JCYJ20190808175201640)。
文摘Multiphoton microscopy is the enabling tool for biomedical research,but the aberrations of biological tissues have limited its imaging performance.Adaptive optics(AO)has been developed to partially overcome aberration to restore imaging performance.For indirect AO,algorithm is the key to its successful implementation.Here,based on the fact that indirect AO has an analogy to the black-box optimization problem,we successfully apply the covariance matrix adaptation evolution strategy(CMA-ES)used in the latter,to indirect AO in multiphoton microscopy(MPM).Compared with the traditional genetic algorithm(GA),our algorithm has a greater improvement in convergence speed and convergence accuracy,which provides the possibility of realizing real-time dynamic aberration correction for deep in vivo biological tissues.
文摘在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learning,CMA-DOL),旨在根据样本点的变化动态更新反向样本点的范围,提高样本多样性,防止陷入局部最优.本方法在分别由10、40、80个发电机组组成的3个测试系统上进行了验证,并与文献中的其他算法进行比较,对超过50次独立运行的结果进行统计度量,实验结果表明CMA-DOL可以获得更好的解决方案.
基金supported by the Provincial Universities Basic Business Expense Scientific Research Projects of Heilongjiang Province(No.2021-KYYWF-0179)the Science and Technology Project of Henan Province(No.212102310991)+2 种基金the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security(No.AGK2015003)the Key Scientific Research Project of Henan Province(No.21A413001)the Postgraduate Innovation Project of Harbin Normal University(No.HSDSSCX2021-121).
文摘To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.
基金supported by the National Natural Science Foundation of China(Nos.11902065,11825202)the Fundamental Research Funds for the Central Universities,China(No.DUT21RC(3)013).
文摘To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally,the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement(MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts,the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.
