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
In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.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
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
Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a si...Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.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 Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
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.
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.
基金Project(52072412)supported by the National Natural Science Foundation of China。
文摘Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.
