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A New Adaptive Prediction a*algorithm for Judicial Sentencing with Empirical Studies

Journal of Systems Science & Complexity 2025年第1期38卷 3-20页

作者： DAI Ruifen WANG Fang GUO Lei ata Science Institute Shandong UniversityJinan250100China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent ***,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread *** to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing *** this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural *** on the saturated structure of our model,a more effective adaptive prediction a*algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning a*algorithm and the construction of a double-hidden-layer network *** empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.

关键词： Adaptive prediction a*algorithm judicial mechanism neural networks sentencing prediction

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An Asynchronous Genetic a*algorithm for Multi-agent Path Planning Inspired by Biomimicry

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Journal of Bionic Engineering 2025年第2期22卷 851-865页

作者： Bin Liu Shikai Jin Yuzhu Li Zhuo Wang Donglai Zhao Wenjie Ge School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an710072China

To address the shortcomings of traditional Genetic a*algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic a*algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering a*algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The a*algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other a*algorithms.

关键词： Multi-agent path planning Asynchronous genetic a*algorithm Equal-size clustering Genetic a*algorithm

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Interpretable gradient boosting based ensemble learning and African vultures optimization a*algorithm optimization for estimating deflection induced by excavation

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Frontiers of Structural and Civil Engineering 2024年第11期18卷 1698-1712页

作者： Zenglong LIANG Shan LIN Miao DONG Xitailang CAO Hongwei GUO Hong ZHENG Key Laboratory of Urban Security and Disaster Engineering(Ministry of Education) Beijing University of TechnologyBeijing 100124China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University(PolyU)Hong Kong 999077China

Intelligent construction has become an inevitable trend in the development of the construction *** the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation *** interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization a*algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by *** investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element ***,we exploratively analyzed these data to discover the relationship between *** used several state-of-the-art intelligent models based on gradient boosting and several simple models for model *** hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness *** comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.

关键词： African vultures optimization a*algorithm gradient boosting ensemble learning interpretable model wall deflection prediction

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Adaptive mutation sparrow search a*algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

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Underground Space 2024年第4期17卷 320-360页

作者： Xiangzhen Zhou Wei Hu Zhongyong Zhang Junneng Ye Chuang Zhao Xuecheng Bian MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058China Hangzhou Canal Group Construction Management Co.LTD Hangzhou 310015China Ningbo Rail Transit Project Construction Headquarter Ningbo 315012China Center for Hypergravity Experimental and Interdisciplinary Research Zhejiang UniversityHangzhou 310058China

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search a*algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft *** novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and *** AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search *** the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ***,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local *** addition,it leads to the improved a*algorithm achieving a better accuracy level and convergence speed compared with the original *** demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization *** statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search *** utilizing the AdaBoost a*algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction ***,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing ***,the predictive outcomes are compared with those of other different optimization and machine learning *** the end,the obtained results in this real-world geotechnical eng

关键词： Adjacent deep excavations Existing subway tunnels Adaptive mutation sparrow search a*algorithm Metaheuristic optimization Benchmark test functions Elman neural networks

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An Adaptive Local Grid Nesting-based Genetic a*algorithm for Multi-earth Observation Satellites' Area Target Observation

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Journal of Systems Science and Systems Engineering 2024年第2期33卷 232-258页

作者： Ligang Xing Wei Xia Xiaoxuan Hu Waiming Zhu Yi Wu School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Inteligent Decision-making Ministry of EducationHefei 230009China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei 230009China

The Scheduling of the Multi-EOSs Area Target Observation(SMEATO)is an EOS resource schedul-ing problem highly coupled with computational *** advances in EOS technology and the ex-pansion of wide-area remote sensing applications have increased the practical significance of *** this paper,an adaptive local grid nesting-based genetic a*algorithm(ALGN-GA)is proposed for developing SMEATO ***,a local grid nesting(LGN)strategy is designed to discretize the target area into parts,so as to avoid the explosive growth of calculations.A genetic a*algorithm(GA)framework is then used to share reserve information for the population during iterative evolution,which can generate high-quality solutions with low computational *** this basis,an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is *** effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different *** results show that the ALGN-GA offers advantages over several conventional a*algorithms in 88.9%of instances,especially in large-scale *** fully demonstrate the high efficiency and stability of the ALGN-GA.

关键词： Multi-EOSs scheduling area target observation adaptive genetic a*algorithm local grid nesting

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Chaotic Aquila Optimization a*algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models

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Journal of Bionic Engineering 2024年第1期21卷 486-526页

作者： Oguz Emrah Turgut Mert Sinan Turgut Erhan Kırtepe Department of Industrial Engineering Faculty of Engineering and ArchitectureIzmir Bakircay UniversityMenemenizmirTurkey Department of Mechanical Engineering Faculty of EngineeringEge UniversityBornovaizmirTurkey Department of Motor Vehicles and Transportation Technologies Sirnak UniversitySirnakTurkey

This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization a*algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random *** work employs 25 different chaotic maps under the framework of Aquila *** considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be studied in past literature *** was found that Ikeda chaotic map enhanced Aquila Optimization a*algorithm yields the best predictions and becomes the leading method in most of the *** test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been ***,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art *** is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining a*algorithms utilized in the performance benchmarking process.

关键词： Aquila optimization a*algorithm Chaotic maps Parameter estimation Phase equilibrium Unconstrained optimization

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Planning of a Single Flow Channel in Valve Blocks Based on Additive Manufacturing and the Ant Colony a*algorithm

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Chinese Journal of Mechanical Engineering 2024年第5期37卷 191-202页

作者： Jin Zhang Ziyang Li Yuying Zhang Yandong Liu Ying Li Xiangdong Kong School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Qinhuangdao 066004China National&Local Joint Engineering Research Center for Advanced Manufacturing Technology and Equipment Qinhuangdao 066004China State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou 310027China

As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks ***,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy ***,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive *** study employed additive manufacturing technology and the ant colony a*algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve *** layout environment for the valve block passages was established,and path optimization was achieved using the ant colony a*algorithm,complemented by smoothing using B-spline ***-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original *** fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the a*algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony a*algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.

关键词： Hydraulic valve block Flow channel B-spline curve Additive manufacturing Ant colony a*algorithm

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Binary Hybrid Artificial Hummingbird with Flower Pollination a*algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Journal of Bionic Engineering 2024年第2期21卷 1003-1021页

作者： Liuyan Feng Yongquan Zhou Qifang Luo College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning532100GuangxiChina Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on *** experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease ***,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer *** this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination a*algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech ***,combining FPA with Artificial Hummingbird a*algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local ***,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic a*algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal ***,an S-shaped function converts the improved a*algorithm into a binary version to suit the characteristics of the feature selection(FS)*** this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease *** with other state-of-the-art a*algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the a*algorithm proposed in this study has apparent advantages in the field of feature selection.

关键词： Artificial Hummingbird a*algorithm Flower pollination a*algorithm Feature selection Parkinson’s disease Meta-heuristic

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Arithmetic optimization a*algorithm with cosine composite chaotic mapping in polar coordinate system for economic load dispatching problems in power systems

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Li, Yi-Xuan Wang, Jie-Sheng Zhang, Si-Wen Zhang, Shi-Hui Guan, Xin-Yi Ma, Xin-Ru Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

Economic load dispatch (ELD) aims to minimize the total cost of generating electricity while satisfying load demand and different operational constraints. The Arithmetic Optimization a*algorithm (AOA) with cosine composite chaotic mapping in polar coordinate system is put forward to solve the ELD problems in the power system with the valve point effect, prohibited operation area, transmission loss and other factors. Firstly, seven polar coordinate system chaotic mappings are proposed to be embedded into the MOP and MOA parameters in the AOA. Secondly, a chaotic system based on the cosine transform is put forward. Then, the proposed cosine transform based chaotic system is combined with polar coordinate system chaotic mapping to form polar coordinate system cosine transform composite chaotic mapping. Eventually, these six polar coordinate system cosine transform composite chaotic mapping is then embedded into the MOA and MOP of the AOA to balance the a*algorithm's global and local search capabilities, improve the performance of the a*algorithm and avoid falling into the local optima. The superiority of the improved a*algorithm is verified by employing 12 benchmark test functions in CEC2022. Then, it is compared with the Coati Optimization a*algorithm (COA), Prairie Dog Optimization (PDO), Butterfly Optimization a*algorithm (BOA), Reptile Search a*algorithm (RSA), Bat a*algorithm (BAT) and Osprey Optimization a*algorithm (OOA) to verify its convergence. The ELD problem for a total demand of 2500 MW is solved by using the AOA with cosine composite chaotic mapping in polar coordinate system. The experimental results show that the improved AOA outperforms other optimization a*algorithms on the 12 benchmark functions in CEC2022 and the ELD problems.

关键词： Economic load dispatching Arithmetic optimization a*algorithm Two-dimensional Chaotic mapping Cosine composite chaotic system Valve point effect

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Adaptive graph neural network protection a*algorithm based on differential privacy

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 225卷

作者： Yu, Junjie Li, Yong Liu, Zhandong Yang, Qianren Xinjiang Normal Univ Coll Comp Sci & Technol Urumqi 830054 Xinjiang Peoples R China

Graph Neural Networks (GNNs) have gained widespread adoption across various fields due to their superior capability in processing graph-structured data. Nevertheless, these models are susceptible to unintentionally disclosing sensitive user information. Current differential privacy a*algorithms for graph neural networks exhibit constrained adaptability and prolonged runtimes. To address these issues, this paper introduces an adaptive GNN protection a*algorithm grounded in differential privacy. The a*algorithm offers robust privacy safeguards at both node and edge levels, employing a bespoke normalization approach based on mean and variance to effectively manage data non-uniformity and outliers, thereby enhancing the model's adaptability to diverse data distributions. Furthermore, the implementation of an early stopping strategy markedly decreases runtime while exerting negligible influence on accuracy, thus enhancing computational efficiency. Experimental results indicate that this approach not only improves the model's predictive accuracy but also significantly reduces its computational time.

关键词： Graph neural networks Differential privacy Adaptive a*algorithm Privacy protection

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