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学者姓名：钟一文
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Abstract ：
Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores-95.3% and 89.5%, respectively-surpassing previous benchmarks. Additionally, we tested the model's transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.
Keyword ：
attention mechanism attention mechanism deep learning deep learning weed detection weed detection YOLOv5s YOLOv5s
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| GB/T 7714 | Zhou, Qi , Li, Huicheng , Cai, Zhiling et al. YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection [J]. | SENSORS , 2025 , 25 (5) . |
| MLA | Zhou, Qi et al. "YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection" . | SENSORS 25 . 5 (2025) . |
| APA | Zhou, Qi , Li, Huicheng , Cai, Zhiling , Zhong, Yiwen , Zhong, Fenglin , Lin, Xiaoyu et al. YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection . | SENSORS , 2025 , 25 (5) . |
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In recent years, multi-objective particle swarm optimization algorithms have been widely used in science and engineering due to their advantages of fast convergence speed and easy implementation. However, the selection of globally optimal particle is an important and challenging problem in the design of multi-objective particle swarm optimization algorithms. In this regard, this paper proposes an adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update, named ADMOPSO. First, an adaptive penalty-based boundary intersection (PBI) distance strategy is designed to select the globally optimal particle from two elite particles which are randomly chosen from an elite particle set. This strategy better balances the diversity and convergence requirements of particle swarm optimization algorithm in the optimization process. Second, a simple position probabilistic update strategy is constructed to rewrite the velocity update method with the weight and use the learning rate to control the scale of the updated velocity in the position update equation to avoid particle swarm falling into the local optimum. Finally, an extensive experimental study is conducted to test the performance of several selected multi-objective optimization algorithms on ZDT, WFG and DTLZ benchmark problems, as well as 7 real-world problems were conducted to test the proposed algorithm. Comparative experimental results show that the algorithm proposed in this paper has significant advantages over other algorithms. This shows that the ADMOPSO algorithm is competitive in dealing with multi-objective problems.
Keyword ：
Adaptive distance Adaptive distance Competitive mechanism Competitive mechanism Multi-objective optimization Multi-objective optimization Particle swarm optimization algorithm Particle swarm optimization algorithm Position update Position update
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| GB/T 7714 | Wang, Liangying , Hong, Lihuan , Fu, Haoxuan et al. Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update [J]. | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 94 . |
| MLA | Wang, Liangying et al. "Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update" . | SWARM AND EVOLUTIONARY COMPUTATION 94 (2025) . |
| APA | Wang, Liangying , Hong, Lihuan , Fu, Haoxuan , Cai, Zhiling , Zhong, Yiwen , Wang, Lijin . Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update . | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 94 . |
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Constructing Steiner Minimum Trees (SMT) remains a critical challenge in Very Large Scale Integration (VLSI) global routing, where minimizing wirelength is essential for optimizing circuit performance. While traditional Manhattan-based SMTs are constrained to two orthogonal routing directions, resulting in suboptimal interconnects, the X architecture, with its eight-directional (four rectilinear, four diagonal) routing, enables significant wirelength reductions. This paper introduces a Heuristic-Guided Scatter Search (HGSS) algorithm to efficiently solve the X-architecture SMT (XSMT) problem. The HGSS integrates a short-edge-first heuristic to prioritize compact routing solutions and reengineers three core Scatter Search modules: (1) a Dynamic Reference Set Update Module (DRSUM) that maintains elite and diverse solutions through iterative replacement, (2) a Semi-systematic Subset Generation Module (SSGM) pairing diverse and random elite solutions to reduce computational overhead, and (3) a Heuristic-Guided Solution Combination Module (HGSCM) employing crossover and mutation to generate high-quality offspring. Evaluations of GEO and ISPD98 benchmark circuits demonstrate average wirelength reductions of 1.04% and 2.86%, respectively, along with superior computational efficiency compared to state-of-the-art methods. By advancing XSMT optimization, this work demonstrates that incorporating heuristic information is valuable for solving large, complex routing tree problems, offering guidance for further research in this area.
Keyword ：
Global Routing (GR) Global Routing (GR) Intelligent computing Intelligent computing Scatter Search (SS) Scatter Search (SS) Steiner Minimum Tree (SMT) Steiner Minimum Tree (SMT) Very Large Scale Integration (VLSI) Very Large Scale Integration (VLSI)
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| GB/T 7714 | Yang, Kai , Zheng, Fuyuan , Ji, Qingjin et al. Heuristic-guided scatter search for X-architecture Steiner Minimum Tree problems in VLSI design [J]. | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 98 . |
| MLA | Yang, Kai et al. "Heuristic-guided scatter search for X-architecture Steiner Minimum Tree problems in VLSI design" . | SWARM AND EVOLUTIONARY COMPUTATION 98 (2025) . |
| APA | Yang, Kai , Zheng, Fuyuan , Ji, Qingjin , Lin, Juan , Zhong, Yiwen , Lin, Yu . Heuristic-guided scatter search for X-architecture Steiner Minimum Tree problems in VLSI design . | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 98 . |
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The min-degree constrained minimum spanning tree (md-MST) problem belongs to a subset of minimum spanning tree (MST) problem variants, imposing a minimum degree constraint for each node. This problem falls within the realm of NP-hard combinatorial optimization problem. In this study, we propose a population-based simulated annealing (PSA) algorithm for the md-MST problem. Initially, a clustering-based initialization with a priority for short edges is devised to define a relatively compact yet high-quality preliminary search space. Following this, three productive neighborhood operators are designed to manage local conformation construction, partial structure refinement, and elite solution information learning. These operators offer a versatile and expansive search space, complementing the subsequent local search process, effectively optimizing the solution. Alongside a global optimal information expansion and coordinated evolution framework, PSA uniformly and flexibly explores the solution space, reaching optimal solutions eventually. Extensive experiments conducted on four widely-used datasets comprising 105 benchmark instances demonstrate that PSA outperforms state-ofthe-art approaches in terms of precision. Specifically, PSA achieves 49 current known best and 41 new best values out of 105 instances.
Keyword ：
Combinatorial optimization Combinatorial optimization Min-degree constrained minimum spanning Min-degree constrained minimum spanning Population-based Population-based Simulated annealing Simulated annealing tree tree
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| GB/T 7714 | Wu, Liangcheng , Yang, Kai , Zhong, Yiwen et al. Population-based simulated annealing algorithm for the min-degree constrained minimum spanning tree problem [J]. | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 92 . |
| MLA | Wu, Liangcheng et al. "Population-based simulated annealing algorithm for the min-degree constrained minimum spanning tree problem" . | SWARM AND EVOLUTIONARY COMPUTATION 92 (2025) . |
| APA | Wu, Liangcheng , Yang, Kai , Zhong, Yiwen , Lin, Juan . Population-based simulated annealing algorithm for the min-degree constrained minimum spanning tree problem . | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 92 . |
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The harmony search (HS) algorithm is a novel metaheuristic which has been widely used to solve both continuous and discrete optimization problems. In order to improve the performance and simplify the implementation of the HS algorithm for solving the 0-1 knapsack problem (0-1KP), this paper proposes a heuristics-guided simplified discrete harmony search (SDHS) algorithm which does not use random search operator and has only one intrinsic parameter, harmony memory size. The SDHS algorithm uses a memory consideration operator to construct a feasible solution, and then the constructed solution is further enhanced by a solution-level pitch adjustment operator. Two heuristics, the profit–weight ratio of an item and the profit of an item, are used to greedily guide the memory consideration operator and the solution-level pitch adjustment operator, respectively. In the memory consideration operator, items are considered in non-ascending order of profit–weight ratio assigned from the harmony memory. In the solution-level pitch adjustment operator, items not in the knapsack are attempted to be selected in non-ascending order of profit. The SDHS algorithm outperforms several state-of-the-art algorithms, with an average improvement of 0.55% in the quality of solutions on large problem instances. © 2025 by the authors.
Keyword ：
Learning algorithms Learning algorithms Optimization Optimization
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| GB/T 7714 | Zheng, Fuyuan , Cheng, Kanglong , Yang, Kai et al. A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem [J]. | Algorithms , 2025 , 18 (5) . |
| MLA | Zheng, Fuyuan et al. "A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem" . | Algorithms 18 . 5 (2025) . |
| APA | Zheng, Fuyuan , Cheng, Kanglong , Yang, Kai , Li, Ning , Lin, Yu , Zhong, Yiwen . A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem . | Algorithms , 2025 , 18 (5) . |
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The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical for aiding physicians in real-time lesion identification and minimizing diagnostic errors such as false positives and missed lesions. Despite significant progress in existing research, accurate segmentation of colorectal polyps remains technically challenging due to persistent issues such as low contrast between polyps and mucosa, significant morphological heterogeneity, and susceptibility to imaging artifacts caused by bubbles in the colorectal lumen and poor lighting conditions. To address these limitations, this study proposed a novel pyramid vision transformer-based hierarchical path aggregation network (HPANet) for polyp segmentation. Specifically, firstly, the backward multi-scale feature fusion module (BMFM) was developed to enhance the ability of processing polyps with different scales. Secondly, the forward noise reduction module (FNRM) was designed to learn the texture features of the upper and lower layers to reduce the influence of noise such as bubbles. Finally, in order to solve the problem of boundary ambiguity caused by repeated up and down sampling, the boundary feature refinement module (BFRM) was developed to further refine the boundary. The proposed network was compared with several representative networks on five public polyp datasets. Experimental results show that the proposed network achieves better segmentation performance, especially on the Kvasir SEG dataset, where the mDice and mIoU coefficients reach 0.9204 and 0.8655. © 2025 by the authors.
Keyword ：
Image segmentation Image segmentation
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| GB/T 7714 | Ying, Yuhong , Li, Haoyuan , Zhong, Yiwen et al. HPANet: Hierarchical Path Aggregation Network with Pyramid Vision Transformers for Colorectal Polyp Segmentation [J]. | Algorithms , 2025 , 18 (5) . |
| MLA | Ying, Yuhong et al. "HPANet: Hierarchical Path Aggregation Network with Pyramid Vision Transformers for Colorectal Polyp Segmentation" . | Algorithms 18 . 5 (2025) . |
| APA | Ying, Yuhong , Li, Haoyuan , Zhong, Yiwen , Lin, Min . HPANet: Hierarchical Path Aggregation Network with Pyramid Vision Transformers for Colorectal Polyp Segmentation . | Algorithms , 2025 , 18 (5) . |
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为解决最小度约束最小生成树问题，提出一种结合强化学习求解的粒子群优化（PSO）算法。在搜索区域初始化过程中，利用生成树结构特征信息，设计基于短边聚类的结构生长方法，为后续搜索提供优质初始解空间；在PSO算法框架内，利用群体协同进化和保留历史信息的特点，设计不同进化速度的学习算子，在求解空间中展开多级精细搜索；设计不同粒度的自主飞行算子，负责不同程度的扰动，提供搜索多样性。同时围绕强化学习的状态反馈机制设计针对不同进化状态的奖惩池，根据当前搜索状态反馈及时调整个体更新策略，实现均衡高效搜索。进一步针对复杂邻域设计针对不同节点关系的两类局部搜索算子，针对叶节点进化设计交换、插入搜索操作，构成最小粒度的局部搜索；针对非叶节点设计替换、删除操作，在保证优质局部结构的同时提供更大范围内的搜索。使用105个被广泛用于测试的实例进行验证及对比，结果表明算法在98个实例上能够达到已知最优解，在其中48个实例中超越现有已知最优解，与其他算法的比较展示了算法的先进性和强有力的竞争力。
Keyword ：
局部搜索 局部搜索 强化学习 强化学习 最小度约束最小生成树 最小度约束最小生成树 粒子群优化 粒子群优化
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| GB/T 7714 | 吴良成 , 杨凯 , 钟一文 et al. 求解最小度约束最小生成树的强化粒子群优化算法 [J]. | 计算机科学与探索 , 2025 , 19 (08) : 2110-2122 . |
| MLA | 吴良成 et al. "求解最小度约束最小生成树的强化粒子群优化算法" . | 计算机科学与探索 19 . 08 (2025) : 2110-2122 . |
| APA | 吴良成 , 杨凯 , 钟一文 , 林娟 . 求解最小度约束最小生成树的强化粒子群优化算法 . | 计算机科学与探索 , 2025 , 19 (08) , 2110-2122 . |
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应对全球碳减排的紧迫挑战，可靠的碳达峰路径对中国碳减排的实施具有重要作用。然而，由于碳排放过程受众多因素影响，且相互作用复杂，传统情景分析方法难以有效识别最优减排路径。为此，在分析福建省的能源消费和碳排放数据的基础上，构建了麻雀搜索算法-支持向量回归模型(Sparrow Search Algorithm-Support Vector Regression, SSA-SVR)模型，该模型综合考虑了影响碳排放的14个关键因素，并基于SVR模型对福建省1999—2022年的碳排放量进行了预测和验证。随后，采用SSA算法优化了各因素的年度变化率组合，探索满足2030年碳达峰目标的多种可能路径。研究结果表明，模型具有较高的准确性和可靠性，探索出的所有路径均能在2030年实现碳达峰，但碳排放量存在显著差异。SSA-SVR模型能够为福建省工业部门实现碳达峰目标提供科学依据和策略建议。
Keyword ：
情景分析 情景分析 碳减排 碳减排 碳达峰路径 碳达峰路径 麻雀搜索算法 麻雀搜索算法
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| GB/T 7714 | 蔡湟 , 林晓宇 , 蔡志铃 et al. 基于麻雀搜索算法的福建省碳达峰路径优化研究 [J]. | 能源环境保护 , 2024 , 38 (03) : 173-183 . |
| MLA | 蔡湟 et al. "基于麻雀搜索算法的福建省碳达峰路径优化研究" . | 能源环境保护 38 . 03 (2024) : 173-183 . |
| APA | 蔡湟 , 林晓宇 , 蔡志铃 , 钟一文 , 钟凤林 . 基于麻雀搜索算法的福建省碳达峰路径优化研究 . | 能源环境保护 , 2024 , 38 (03) , 173-183 . |
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In this paper, we address the prize-collecting generalized minimum spanning tree problem (PC-GMSTP) which aims to find a minimum spanning tree to connect a network of clusters using exactly one vertex per cluster, minimizing the total cost of connecting the clusters while considering both the costs of edges and the prizes offered by the vertices. An Adaptive Multi-meme Memetic Algorithm (AMMA) is proposed to tackle PC-GMSTP, which combines an adaptive reproduction procedure and a collaborated local search procedure. The adaptive reproduction procedure uses crossover or mutation to produce offspring independently to maintain a good balance between exploration and exploitation of the search space, and the probability to use crossover or mutation is adaptively adjusted based on the diversity of population. The collaborated local search procedure, which includes two efficient local search operators, can effectively enhance the intensification ability of AMMA due to their complementary features. Extensive computational experiments on 126 challenging instances demonstrate the superiority of AMMA, outperforming 23 best-known solutions from existing literature while achieving similar solutions for the remaining 103 instances. Wilcoxon’s test confirms that the performance of AMMA is significantly better than the state-of-the-art algorithms. © 2024, The Authors. All rights reserved.
Keyword ：
Cell proliferation Cell proliferation Clustering algorithms Clustering algorithms Local search (optimization) Local search (optimization) Trees (mathematics) Trees (mathematics)
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| GB/T 7714 | Zhu, Chenwei , Lin, Yu , Zheng, Fuyuan et al. An Adaptive Multi-Meme Memetic Algorithm for the Prize-Collecting Generalized Minimum Spanning Tree Problem [J]. | SSRN , 2024 . |
| MLA | Zhu, Chenwei et al. "An Adaptive Multi-Meme Memetic Algorithm for the Prize-Collecting Generalized Minimum Spanning Tree Problem" . | SSRN (2024) . |
| APA | Zhu, Chenwei , Lin, Yu , Zheng, Fuyuan , Lin, Juan , Zhong, Yiwen . An Adaptive Multi-Meme Memetic Algorithm for the Prize-Collecting Generalized Minimum Spanning Tree Problem . | SSRN , 2024 . |
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This paper addresses the problem of mapping, scheduling, and routing of virtual network functions (VNF) on a service function chain (SFC) that is sensitive to latency in a virtual network. A scheduling algorithm for VNF is proposed, which aims to minimize the SFC rejection rate while taking into account VNF mapping, scheduling, and traffic routing during the scheduling process. To achieve this goal, a Markov decision process (MDP)-based VNF scheduling model is established that guarantees SFC resource requirements are met. The model uses the D3QN (Dueling Double DQN) algorithm based on composite rules to select the SFC at each scheduling time point, and selects virtual nodes and routes using a routing optimization algorithm to minimize the SFC rejection rate. We compare our algorithm with the single rule, DQN and genetic algorithm, and the simulation results show that the proposed algorithm can reduce the rejection rate of SFC by approximately 8% compared to genetic algorithms.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Delay-aware Delay-aware Service function chain Service function chain Virtual network functions Virtual network functions VNF scheduling VNF scheduling
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| GB/T 7714 | Liu, Zhiwei , Shu, Zhaogang , Chen, Shuwu et al. Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning [J]. | COMPUTER NETWORKS , 2024 , 246 . |
| MLA | Liu, Zhiwei et al. "Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning" . | COMPUTER NETWORKS 246 (2024) . |
| APA | Liu, Zhiwei , Shu, Zhaogang , Chen, Shuwu , Zhong, Yiwen , Lin, Jiaxiang . Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning . | COMPUTER NETWORKS , 2024 , 246 . |
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