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Multi-strategy arithmetic optimization a*algorithm for global optimization and uncertain motion tracking

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-28页

作者： Gao, Zeng Zhuang, Yi Gu, Jingjing Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Jiangsu Peoples R China

Previous trackers mostly assume that the target has a smooth motion and perform target matching within a local window. However, targets often exhibit uncertain movements in real-world scenarios. Once the tracked target undergoes abrupt motion and moves outside the predefined local window, these trackers often fail. To address this issue, this paper introduces a multi-strategy arithmetic optimization a*algorithm (MSAOA) for global optimization and uncertain motion tracking. MSAOA is a high-performance optimizer that effectively solves uncertain motion in visual tracking. For MSAOA, we first design a dynamic stratification strategy to divide the population into three subpopulations. Then the mathematical model of each subpopulation is modified to improve the exploration and exploitation performance. Finally, extensive experiments over 23 benchmark functions and CEC2020 benchmark problems show that MSAOA is better than other a*algorithms. For the MSAOA tracker (MSAOAT), we utilize the proposed MSAOA as a joint local sampling-global search to generate candidate targets and match the best targets by a fitness function. More importantly, we design a verifier to unite local sampling and global search to form a complete tracking framework, which can effectively address smooth and abrupt motion in visual tracking. The qualitative and quantitative analyses on the general motion group and the abrupt motion group demonstrate that the MSAOAT can outperform other trackers.

关键词： Uncertain motion tracking Arithmetic optimization a*algorithm Dynamic stratification strategy Multi-strategy

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An adaptive genetic hyper-heuristic a*algorithm for a two-echelon vehicle routing problem with dual-customer satisfaction in community group-buying

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 194卷

作者： Xu, Song Ou, Xiangyue Govindan, Kannan Chen, Mingzhou Yang, Wenting Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Univ Adelaide Inst Sustainabil Energy & Resources Ctr Sustainable Operat & Resilient Supply Chains C Adelaide Business Sch Adelaide SA 5005 Australia Shanghai Univ Int Business & Econ Sch Management Shanghai 201620 Peoples R China Tongji Univ Sch Econ & Management Shanghai Peoples R China

This study focuses on a novel variant of the classical two-echelon vehicle routing problem (2EVRP), termed the two-echelon vehicle routing problem with dual-customer satisfaction (2E-VRPDS) (i.e. time windows satisfaction and freshness satisfaction) in community group-buying. It is important to obtain better solutions for the 2E-VRP-DS with long-distance distribution in the first echelon and last-mile delivery in the second echelon. Therefore, a new mathematical model is established for the 2E-VRP-DS that incorporates objectives: minimising the total distribution costs, and maximum dual-customer satisfaction (time windows satisfaction, and product freshness satisfaction). To solve the mathematical model, an efficient adaptive genetic hyper-heuristic a*algorithm (AGA-HH) was proposed, complemented by a k-means clustering approach to generate initial solutions. The adaptive genetic a*algorithm is considered to be a high-level heuristic, and ten local search operators were considered as low-level heuristics to expand the search region of the solution and achieve robust optimal results. Three sets of experiments were conducted, and the results demonstrated the superiority of AGA-HH in solving the 2E-VRP-DS, showing improvements in distribution costs reduction, time windows compliance, and product freshness preservation. Moreover, sensitivity analyses were carried out to show the influence of the number of DCs and the tolerance range of product freshness, discovering some managerial insights for companies. Future work should consider and investigate VRPs in other new business modes.

关键词： Two-echelon vehicle routing problem Dual-customer satisfaction Multi-temperature joint distribution Adaptive genetic hyper-heuristic a*algorithm

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Multipath Estimation of Navigation Signals Based on Extended Kalman Filter-Genetic a*algorithm Particle Filter a*algorithm

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3851-3851页

作者： Li, Jie Sun, Xiyan Ji, Yuanfa Li, Jingjing Li, Long Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Guilin Univ Elect Technol Informat & Commun Sch Guilin 541004 Peoples R China Guilin Univ Elect Technol Intelligent Locat Serv Int Joint Res Lab Spatio Temporal Informat Guilin 541004 Peoples R China GUET Nanning Etech Res Inst Co Ltd Nanning 530031 Peoples R China

The particle filter (PF) a*algorithm has found widespread application in navigation multipath estimation. However, it exhibits significant limitations in complex multipath environments. Its state prediction relies heavily on particle distribution and is prone to particle degeneracy, where the weights of most particles approach zero, and only a few particles contribute significantly to state estimation. These issues result in an inadequate number of effective samples, degrading multipath estimation performance. Therefore, a navigation multipath estimation method based on an EKF-GAPF (Extended Kalman Filter-Genetic a*algorithm Particle Filter) a*algorithm is proposed in this paper. This method utilizes the EKF to calculate the mean and covariance of samples using the latest observation information, providing a more reasonable proposal density for particle filtering and enhancing the accuracy of state prediction. Simultaneously, by introducing the crossover and mutation mechanisms of the adaptive genetic a*algorithm, particles are continuously evolved during the resampling process, preventing them from falling into local extrema. Experimental results show that EKF-GAPF outperforms EKF, EPF, and PF in amplitude and delay estimation. Under the condition of random initial values, the multipath signal amplitude estimation error converges to 0.002, and the multipath signal time delay estimation error converges to 0.006 (approximately 1.8 m). This method enables high-precision parameter estimation for both direct and multipath signals.

关键词： multipath estimation particle filtering extended Kalman filter adaptive genetic a*algorithm

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Path planning of USV in confined waters based on improved A * and DWA fusion a*algorithm

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OCEAN ENGINEERING 2025年 322卷

作者： Zhang, Jiadong Ling, Hongjie Tang, Zhihao Song, Wandi Lu, Anqi Jiangsu Univ Sci & Technol Sch Shipbuilding & Ocean Engn Zhenjiang 212003 Peoples R China

In the field of unmanned vessels, path planning in confined and complex environments has become a crucial research focus. Existing methods face issues such as insufficient obstacle avoidance and low planning efficiency. To address these challenges, this paper proposes a hybrid approach combining an improved A* a*algorithm with an optimized Dynamic Window Approach (DWA). The enhanced A* a*algorithm adjusts the weight of the heuristic function by introducing a tuning factor (alpha), which directly influences the obstacle density. Additionally, a 5neighborhood search combined with the Floyd a*algorithm is employed to boost search efficiency and improve path smoothness. The modified DWA a*algorithm incorporates a path smoothing coefficient and a local target selection strategy, enhancing the safety and stability of local planning. MATLAB simulations demonstrate that the proposed hybrid a*algorithm generates smooth and safe paths, successfully avoids dynamic obstacles, and shows promising effectiveness and feasibility in unmanned vessel path planning.

关键词： Unmanned surface vehicle Path planning A* a*algorithm DWA a*algorithm Dynamic obstacle avoidance Fusion a*algorithm

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EAEFA-R: Multiple learning-based ensemble artificial electric field a*algorithm for global optimization

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KNOWLEDGE-BASED SYSTEMS 2025年 318卷

作者： Chauhan, Dikshit Yadav, Anupam Mallipeddi, Rammohan Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math & Comp Jalandhar 144008 Punjab India Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

Adjusting the search behaviors of swarm-based a*algorithms is crucial for solving real-world optimization challenges. Researchers have developed ensemble strategies and self-adaptive mechanisms to enhance the optimization ability of individual a*algorithms by balancing global and local search capabilities. Inspired by these advancements, this paper proposes a physics-based artificial electric field a*algorithm with three improvement strategies and an attraction-repulsion operator (EAEFA-R) to enhance diversity and escape local optima. These strategies are probabilistically selected using a dynamic adaptation mechanism. The effectiveness of EAEFA-R is assessed through extensive analysis of exploration-exploitation dynamics and diversity, and it is evaluated on two real-parameter test suites, CEC 2017 and CEC 2022, across 10, 20, 30, 50, and 100-dimensional search spaces. Compared to fifteen state-of-the-art a*algorithms, including AEFA variants and other optimization a*algorithms, EAEFA-R demonstrates superior solution accuracy, convergence rate, search capability, and stability performance. The overall ranking highlights its exceptional potential for solving challenging optimization problems, outperforming other state-of-the-art a*algorithms across various dimensions. The MATLAB source code of EAEFA-R is available at https://***/ChauhanDikshit.

关键词： Meta-heuristic a*algorithms Optimization Ensembling Repulsion strategy Artificial electric field a*algorithm

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Optimization of chlorine consumption in water distribution networks by using the new ant colony optimization (ACOR) a*algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2025年第5期22卷 3199-3212页

作者： Ahmadi, M. H. Mansoori, B. Aghamajidi, R. Islamic Azad Univ Dept Civil Engn Beyza Branch Beyza Iran Islamic Azad Univ Meymand Ctr Dept Civil Engn Firoozabad Branch Meymand Iran Islamic Azad Univ Dept Civil Engn Sepidan Branch Sepidan Iran

Chlorination by maintaining the injected chlorine concentration in the range between the minimum and maximum is among the most inexpensive and common disinfection methods in water distribution networks. The minimum concentration of residual chlorine must be observed to control the microbial quality of water. Besides, the maximum chlorine concentration must be observed to control problems related to water smell and taste and to prevent the production of toxic byproducts. This research has developed a model by combining the EPANET model and the ACOR optimization a*algorithm to optimize the chlorine injection program during the operation period. According to the results, the ACOR a*algorithm could be used to derive a suitable program for chlorine injection in the water distribution network such that the permissible constraints of chlorine are observed in the consumption nodes of the network and the consumption of chlorine is reduced to the least level in the network. The developed model was applied to determine an optimal chlorine injection program in a classical example (the Branford network), which was also of interest to some previous researchers. Using the optimal injection program obtained by the model, the chlorine concentration was set at an acceptable network level between the permissible range of 0.2-0.8 g/l. This output was more favorable than the response of other methods in terms of the total residual chlorine concentration, which was 5.8% and 4.7% lower in this method than the methods based on PSO and genetic a*algorithms, respectively. Moreover, a better convergence speed was obtained in this a*algorithm, and the number of calculation times of the objective function was 49.5 and 64.4 less than the methods based on PSO and genetic a*algorithms, respectively. Therefore, the ACOR a*algorithm can be used to derive the chlorine injection program to both comply with the permissible constraints of chlorine and reduce chlorine consumption to the minimum level.

关键词： ACOR a*algorithm Water distribution networks Water quality control EPANET

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Polynomial fuzzy information granule based α-triple I fuzzy reasoning a*algorithm and its short-term forecasting of time series

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INFORMATION SCIENCES 2025年 689卷

作者： Li, Fang Wang, Caifang Shanghai Maritime Univ Sch Sci Shanghai 201306 Peoples R China

Short-term forecasting of time series is an important research topic, which involves data characteristic capture and intelligent reasoning. For this topic, the Gaussian polynomial fuzzy information granule with interpretability is granulated on data, enabling the extraction of data trend characteristic, besides, the associate characteristic within the data is captured through building fuzzy association rule. Building upon the data characteristics captured in fuzzy information granule and fuzzy association rule, an intelligent reasoning a*algorithm called the fuzzy information granule based alpha-Triple I a*algorithm is proposed, where the membership degree of data to granule is considered in reasoning, and next the accurate level of deviation from data to granule can be inferenced. Based on the excavated data characteristics and a rational inference, a short-term forecasting model is established. Its superiority in terms of accuracy and reliability when compared to 7 other models in real time series has been tested. Notably, the prediction of the novel model is accurate because the function of FAR is identified from FAR's truth degree, which means the validity degree of prediction. The application of the proposed model for short-term forecasting holds a potential impact across various fields.

关键词： Polynomial fuzzy information granule alpha-Triple I a*algorithm Fuzzy association rule Fuzzy reasoning system Short-term forecasting

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Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization a*algorithm

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COMPUTER STANDARDS & INTERFACES 2025年 92卷

作者： Deepa, R. Karthick, R. Velusamy, Jayaraj Senthilkumar, R. Nehru Inst Engn & Technol Dept Elect & Commun Engn Coimbatore Tamilnadu India KLN Coll Engn Dept Comp Sci & Engn Pottapalayam 630612 India Hindusthan Inst Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

This research aims to optimize the interference mitigation and improve system performance metrics, such as bit error rates, inter-carrier interference (ICI), and inter-symbol interference (ISI), by integrating the Redundant Discrete Wavelet Transform (RDWT) with the Arithmetic Optimization a*algorithm (AOA). This will increase the spectral efficiency of MIMO-OFDM systems for ultra-high data rate (UHDR) transmission in 5 G networks. The most important contribution of this study is the innovative combination of RDWT and AOA, which effectively addresses the down sampling issues in DWT-OFDM systems and significantly improves both error rates and data rates in high-speed wireless communication. Fifth-generation wireless networks require transmission at ultrahigh data rates, which necessitates reducing ISI and ICI. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is employed to achieve the UHDR. The bandwidth and orthogonality of DWT-OFDM (discrete wavelet transform-based OFDM) are increased;however system performance is degraded due to down sampling. The redundant discrete wavelet transform (RDWT) is proposed for eliminating down sampling complexities. Simulation results demonstrate that RDWT effectively lowers bit error rates, ICI, and ISI by increasing the carrier-to-interference power ratio (CIR). The Arithmetic Optimization a*algorithm is used to optimize ICI cancellation weights, further enhancing spectrum efficiency. The proposed method is executed in MATLAB and achieves notable performance gains: up to 82.95% lower error rates and 39.88 % higher data rates compared to the existing methods. Conclusion: The integration of RDWT with AOA represents a significant advancement in enhancing the spectral efficiency of MIMO-OFDM systems for UHDR transmission in 5 G networks. The proposed method not only enhances system performance but also lays a foundation for future developments in high-speed wireless communication by addressing down sampl

关键词： Redundant discrete wavelet transform Arithmetic optimization a*algorithm Enhanced spectral efficiency Discrete wavelet transform

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Artificial satellite search: A new metaheuristic a*algorithm for optimizing truss structure design and project scheduling

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APPLIED MATHEMATICAL MODELLING 2025年 143卷

作者： Cheng, Min-Yuan Sholeh, Moh Nur Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43 Sec 4Keelung Rd Taipei 106 Taiwan

The Artificial Satellite Search a*algorithm (ASSA), a novel physics-based metaheuristic a*algorithm designed to emulate the dynamic motion of satellites within a search space, is introduced in this study. The ASSA uses satellites as candidate solutions, which dynamically update their positions to navigate toward the optimal solution. The a*algorithm simulates satellite behavior using medium Earth orbit and low Earth orbit trajectories, facilitating more effective exploration and exploitation of the search space by accounting for the diverse scenario's satellites encounter relative to the Earth over time. In addition, orbit control mechanism and quantum computing technique are incorporated into the ASSA to further enhance the computational efficiency. Two experiments were conducted to assess ASSA performance. First, the performances of ASSA and seven wellknown a*algorithms were benchmarked on thirty benchmark functions and the CEC-2020 test suite. ASSA outperformed all of the comparison a*algorithms on the Wilcoxon signed-rank test, earned the highest rank (scoring 2.21 and 3.27 on the thirty benchmark and CEC-2020 test suite functions, respectively) on the Friedman test, and solved 27 out of 30 functions with shorter computational times. Second, ASSA was applied to address three engineering problems, achieving the best weight for truss structure optimization and the highest success rates for project scheduling. In these practical engineering applications, ASSA not only exhibited superior performance compared to alternative methods but also required the fewest evaluations of objective functions. The robustness and ease of implementation of the ASSA makes this new a*algorithm a versatile solution for various numerical optimization challenges.

关键词： Artificial satellite search a*algorithm Metaheuristic optimization Orbit control mechanism Quantum computing Project scheduling Truss structure design

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A Dynamic Nuclear Radiation Environment Path Planning Method Based on an Improved Ant Colony a*algorithm

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NUCLEAR TECHNOLOGY 2025年

作者： Xiang, Suo-Yi Zhou, Huai-Fang Huo, Jian-Wen Zhang, Hua Gu, Chao-Fan Southwest Univ Sci & Technol Sch Informat & Engn Mianyang 621010 Sichuan Peoples R China Southwest Univ Sci & Technol Sichuan Key Lab Special Environm Robot Mianyang 621010 Sichuan Peoples R China State Grid Guangyuan Power Supply Co Guangyuan 628000 Peoples R China

Minimum cumulative dose path planning is an important radiation protection measure to reduce the radiation exposure of robots in nuclear emergencies. However, when an emergency or accident occurs, the distribution of radiation doses in the environment changes dynamically, making the cumulative radiation dose of paths planned by traditional methods nonoptimal. This study proposes a Dijkstra-improved ant colony optimization a*algorithm (DIACO) to address this issue, combined with a segmented search method to achieve path planning in a dynamic radiation *** method transforms the minimal cumulative radiation dose path obtained by the Dijkstra a*algorithm into an increment of the initial pheromone distribution for the ant colony optimization (ACO) a*algorithm, improves the heuristic factor of the ACO a*algorithm, and incorporates the maximum-minimum ant system to enhance the a*algorithm's convergence *** results show that the proposed DIACO a*algorithm reduces the cumulative radiation dose of the obtained path by approximately 21.08%, the travel distance to the target by about 33.87%, and the number of turns by about 85.1% compared to the traditional ACO a*algorithm.

关键词： Minimum cumulative dose path planning radiation exposure radiation protection ant colony optimization a*algorithm dynamic radiation environment

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