Complex & Intelligent Systems最新文献_第4页

Sentimentally enhanced conversation recommender system 情感增强对话推荐系统

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-08 DOI: 10.1007/s40747-024-01766-9

Fengjin Liu, Qiong Cao, Xianying Huang, Huaiyu Liu

{"title":"Sentimentally enhanced conversation recommender system","authors":"Fengjin Liu, Qiong Cao, Xianying Huang, Huaiyu Liu","doi":"10.1007/s40747-024-01766-9","DOIUrl":"https://doi.org/10.1007/s40747-024-01766-9","url":null,"abstract":"Conversation recommender system (CRS) aims to provide high-quality recommendations to users in fewer conversation turns. Existing studies often rely on knowledge graphs to enhance the representation of entity information. However, these methods tend to overlook the inherent incompleteness of knowledge graphs, making it challenging for models to fully capture users’ true preferences. Additionally, they fail to thoroughly explore users’ emotional tendencies toward entities or effectively differentiate the varying impacts of different entities on user preferences. Furthermore, the responses generated by the dialogue module are often monotonous, lacking diversity and expressiveness, and thus fall short of meeting the demands of complex scenarios. To address these shortcomings, we propose an innovative Sentimentally Enhanced Conversation Recommender System (SECR). First, we construct a comprehensive and highly optimized knowledge graph, termed MAKG, which provides a rich and complete set of entities to help the model capture user preferences more holistically. This significantly improves the inference depth and decision accuracy of the recommender system. Second, by deeply analyzing the emotional semantics in dialogues, the system accurately identifies users’ emotional tendencies toward entities and recommends those that best align with their preferences. To refine the recommendation strategy, we design an emotional weighting mechanism to quantify and distinguish the importance of different entities in shaping user preferences. Lastly, we develop an efficient text filter to extract movie introductions from external data sources and integrate them into the dialogue, greatly enhancing the diversity and semantic richness of the generated responses. Extensive experimental results on two public CRS datasets demonstrate the effectiveness of our approach. Our code is released on https://github.com/Janns0916/EECR.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"203 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand 需求不确定多式联运情况下改进的多目标路线优化算法的实践

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-08 DOI: 10.1007/s40747-024-01725-4

Tianxu Cui, Ying Shi, Jingkun Wang, Rijia Ding, Jinze Li, Kai Li

{"title":"Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand","authors":"Tianxu Cui, Ying Shi, Jingkun Wang, Rijia Ding, Jinze Li, Kai Li","doi":"10.1007/s40747-024-01725-4","DOIUrl":"https://doi.org/10.1007/s40747-024-01725-4","url":null,"abstract":"In recent decades, multimodal transportation has played a crucial role in modern logistics and transportation systems because of its high capacity and low cost. However, multimodal transportation driven mainly by fossil fuels may result in significant carbon emissions. In addition, transportation costs, transportation efficiency, and customer demand are also key factors that constrain the development of multimodal transportation. In this paper, we develop, for the first time, a many-objective multimodal transportation route optimization (MTRO) model that simultaneously considers economic cost, carbon emission cost, time cost, and customer satisfaction, and we solve it via the nondominated sorting genetic algorithm version III (NSGA-III). Second, to further improve the convergence performance, we introduce a fuzzy decision variable framework to improve the NSGA-III algorithm. This framework can reduce the search range of the optimization algorithm in the decision space and make it converge better. Finally, we conduct numerous simulation experiments on test problems to verify the applicability and superiority of the improved algorithm and apply it to MTRO problems under uncertain demand. This work fills the research gap for MTRO problems and provides guidance for relevant departments in developing transportation and decarbonization plans.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"24 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems 半监督学习技术辅助多目标进化算法求解计算量大的问题

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01715-6

Zijian Jiang, Chaoli Sun, Xiaotong Liu, Hui Shi, Sisi Wang

{"title":"A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems","authors":"Zijian Jiang, Chaoli Sun, Xiaotong Liu, Hui Shi, Sisi Wang","doi":"10.1007/s40747-024-01715-6","DOIUrl":"https://doi.org/10.1007/s40747-024-01715-6","url":null,"abstract":"Existing multi-objective evolutionary algorithms (MOEAs) have demonstrated excellent efficiency when tackling multi-objective tasks. However, its use in computationally expensive multi-objective issues is hindered by the large number of reliable evaluations needed to find Pareto-optimal solutions. This paper employs the semi-supervised learning technique in model training to aid in evolutionary algorithms for addressing expensive multi-objective issues, resulting in the semi-supervised learning technique assisted multi-objective evolutionary algorithm (SLTA-MOEA). In SLTA-MOEA, the value of every objective function is determined as a weighted mean of values approximated by all surrogate models for that objective function, with the weights optimized through a convex combination problem. Furthermore, the number of unlabelled solutions participating in model training is adaptively determined based on the objective evaluations conducted. A group of tests on DTLZ test problems with 3, 5, and 10 objective functions, combined with a practical application, are conducted to assess the effectiveness of our proposed method. Comparative experimental results versus six state-of-the-art evolutionary algorithms for expensive problems show high efficiency of SLTA-MOEA, particularly for problems with irregular Pareto fronts.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RenalSegNet: automated segmentation of renal tumor, veins, and arteries in contrast-enhanced CT scans RenalSegNet：在增强CT扫描中自动分割肾肿瘤、静脉和动脉

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01751-2

Rashid Khan, Chao Chen, Asim Zaman, Jiayi Wu, Haixing Mai, Liyilei Su, Yan Kang, Bingding Huang

{"title":"RenalSegNet: automated segmentation of renal tumor, veins, and arteries in contrast-enhanced CT scans","authors":"Rashid Khan, Chao Chen, Asim Zaman, Jiayi Wu, Haixing Mai, Liyilei Su, Yan Kang, Bingding Huang","doi":"10.1007/s40747-024-01751-2","DOIUrl":"https://doi.org/10.1007/s40747-024-01751-2","url":null,"abstract":"Renal carcinoma is a frequently seen cancer globally, with laparoscopic partial nephrectomy (LPN) being the primary form of treatment. Accurately identifying renal structures such as kidneys, tumors, veins, and arteries on CT scans is crucial for optimal surgical preparation and treatment. However, the automatic segmentation of these structures remains challenging due to the kidney's complex anatomy and the variability of imaging data. This study presents RenalSegNet, a novel deep-learning framework for automatically segmenting renal structure in contrast-enhanced CT images. RenalSegNet has an innovative encoder-decoder architecture, including the FlexEncoder Block for efficient multivariate feature extraction and the MedSegPath mechanism for advanced feature distribution and fusion. Evaluated on the KiPA dataset, RenalSegNet achieved remarkable performance, with an average dice score of 86.25%, IOU of 76.75%, Recall of 86.69%, Precision of 86.48%, HD of 15.78 mm, and AVD of 0.79 mm. Ablation studies confirm the critical roles of the MedSegPath and MedFuse components in achieving these results. RenalSegNet's robust performance highlights its potential for clinical applications and offers significant advances in renal cancer treatment by contributing to accurate preoperative planning and postoperative evaluation. Future improvements to model accuracy and applicability will involve integrating advanced techniques, such as unsupervised transformer-based approaches.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balanced coarse-to-fine federated learning for noisy heterogeneous clients 针对有噪声的异构客户机的平衡的从粗到精的联邦学习

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01694-8

Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

{"title":"Balanced coarse-to-fine federated learning for noisy heterogeneous clients","authors":"Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang","doi":"10.1007/s40747-024-01694-8","DOIUrl":"https://doi.org/10.1007/s40747-024-01694-8","url":null,"abstract":"For heterogeneous federated learning, each client cannot ensure the reliability due to the uncertainty in data collection, where different types of noise are always introduced into heterogeneous clients. Current existing methods rely on the specific assumptions for the distribution of noise data to select the clean samples or eliminate noisy samples. However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. To overcome these challenges, we propose a balanced coarse-to-fine federated learning method to solve noisy heterogeneous clients. By introducing the coarse-to-fine two-stage strategy, the client can adaptively eliminate the noisy data. Meanwhile, we proposed a balanced progressive learning framework, It leverages the self-paced learning to sort the training samples from simple to difficult, which can evenly construct the client model from simple to difficult paradigm. The experimental results show that the proposed method has higher accuracy and robustness in processing noisy data from heterogeneous clients, and it is suitable for both heterogeneous and homogeneous federated learning scenarios. The code is avaliable at https://github.com/drafly/bcffl.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection Batch-in-Batch：一种新的针对初始扰动和样本选择的对抗训练框架

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01704-9

Yinting Wu, Pai Peng, Bo Cai, Le Li

{"title":"Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection","authors":"Yinting Wu, Pai Peng, Bo Cai, Le Li","doi":"10.1007/s40747-024-01704-9","DOIUrl":"https://doi.org/10.1007/s40747-024-01704-9","url":null,"abstract":"Adversarial training methods commonly generate initial perturbations that are independent across epochs, and obtain subsequent adversarial training samples without selection. Consequently, such methods may limit thorough probing of the vicinity around the original samples and possibly lead to unnecessary or even detrimental training. In this work, a simple yet effective training framework, called Batch-in-Batch (BB), is proposed to refine adversarial training from these two perspectives. The framework jointly generates m sets of initial perturbations for each original sample, seeking to provide high quality adversarial samples by fully exploring the vicinity. Then, it incorporates a sample selection procedure to prioritize training on higher-quality adversarial samples. Through extensive experiments on three benchmark datasets with two network architectures in both single-step (Noise-Fast Gradient Sign Method, N-FGSM) and multi-step (Projected Gradient Descent, PGD) scenarios, models trained within the BB framework consistently demonstrate superior adversarial accuracy across various adversarial settings, notably achieving an improvement of more than 13% on the SVHN dataset with an attack radius of 8/255 compared to N-FGSM. The analysis further demonstrates the efficiency and mechanisms of the proposed initial perturbation design and sample selection strategies. Finally, results concerning training time indicate that the BB framework is computational-effective, even with a relatively large m.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"66 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TMFN: a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning for multimodal sentiment analysis TMFN：一种基于文本的多模态融合网络，具有多尺度特征提取和无监督对比学习，用于多模态情感分析

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01724-5

Junsong Fu, Youjia Fu, Huixia Xue, Zihao Xu

{"title":"TMFN: a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning for multimodal sentiment analysis","authors":"Junsong Fu, Youjia Fu, Huixia Xue, Zihao Xu","doi":"10.1007/s40747-024-01724-5","DOIUrl":"https://doi.org/10.1007/s40747-024-01724-5","url":null,"abstract":"Multimodal sentiment analysis (MSA) is crucial in human-computer interaction. Current methods use simple sub-models for feature extraction, neglecting multi-scale features and the complexity of emotions. Text, visual, and audio each have unique characteristics in MSA, with text often providing more emotional cues due to its rich semantics. However, current approaches treat modalities equally, not maximizing text’s advantages. To solve these problems, we propose a novel method named a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning (TMFN). Firstly, we propose an innovative pyramid-structured multi-scale feature extraction method, which captures the multi-scale features of modal data through convolution kernels of different sizes and strengthens key features through channel attention mechanism. Second, we design a text-based multimodal feature fusion module, which consists of a text gating unit (TGU) and a text-based channel-wise attention transformer (TCAT). TGU is responsible for guiding and regulating the fusion process of other modal information, while TCAT improves the model’s ability to capture the relationship between features of different modalities and achieves effective feature interaction. Finally, to further optimize the representation of fused features, we introduce unsupervised contrastive learning to deeply explore the intrinsic connection between multi-scale features and fused features. Experimental results show that our proposed model outperforms the state-of-the-art models in MSA on two benchmark datasets.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"37 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adjoint feature-selection-based evolutionary algorithm for sparse large-scale multiobjective optimization 基于伴随特征选择的稀疏大规模多目标优化进化算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01752-1

Panpan Zhang, Hang Yin, Ye Tian, Xingyi Zhang

{"title":"An adjoint feature-selection-based evolutionary algorithm for sparse large-scale multiobjective optimization","authors":"Panpan Zhang, Hang Yin, Ye Tian, Xingyi Zhang","doi":"10.1007/s40747-024-01752-1","DOIUrl":"https://doi.org/10.1007/s40747-024-01752-1","url":null,"abstract":"Sparse large-scale multiobjective optimization problems (sparse LSMOPs) are characterized by an enormous number of decision variables, and their Pareto optimal solutions consist of a majority of decision variables with zero values. This property of sparse LSMOPs presents a great challenge in terms of how to rapidly and precisely search for Pareto optimal solutions. To deal with this issue, this paper proposes an adjoint feature-selection-based evolutionary algorithm tailored for tackling sparse LSMOPs. The proposed optimization strategy combines two distinct feature selection approaches. Specifically, the paper introduces the sequential forward selection approach to investigate independent sparse distribution, denoting it as the best sequence of decision variables for generating a high-quality initial population. Furthermore, it introduces the Relief approach to determine the relative sparse distribution, identifying crucial decisive variables with dynamic updates to guide the population in a promising evolutionary direction. Experiments are conducted on eight benchmark problems and two real-world problems, and experimental results verify that the proposed algorithm outperforms the existing state-of-the-art evolutionary algorithms for solving sparse LSMOPs.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"98 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Light-YOLO: a lightweight detection algorithm based on multi-scale feature enhancement for infrared small ship target Light-YOLO：一种基于多尺度特征增强的红外小型舰船目标轻量化检测算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01726-3

Ji Tang, Xiao-Min Hu, Sang-Woon Jeon, Wei-Neng Chen

{"title":"Light-YOLO: a lightweight detection algorithm based on multi-scale feature enhancement for infrared small ship target","authors":"Ji Tang, Xiao-Min Hu, Sang-Woon Jeon, Wei-Neng Chen","doi":"10.1007/s40747-024-01726-3","DOIUrl":"https://doi.org/10.1007/s40747-024-01726-3","url":null,"abstract":"Infrared-based detection of small targets on ships is crucial for ensuring navigation safety and effective maritime traffic management. However, existing ship target detection models often encounter missed detections and struggle to achieve both high accuracy and real-time performance at the same time. Addressing these challenges, this study presents Light-YOLO, a lightweight model for ship small target detection. Within the YOLOv8 network architecture, Light-YOLO replaces conventional convolutions with snake convolutions, effectively addressing the issue of inadequate detection point receptive fields for small targets, thereby enhancing their detection. Additionally, a Multi-Scale Feature Enhancement Module (MFEB) is introduced to refine focus on low-level features through multi-scale and selection strategies, mitigating issues such as interference from image backgrounds and noise during small target detection. Furthermore, a novel loss function is designed to dynamically adjust the proportions of its components during training, improving the regression accuracy of small targets towards real annotation boxes and enhancing the localization ability of detection boxes. Experimental results demonstrate that Light-YOLO outperforms YOLOv8n, achieving optimal performance on an infrared ship small target detection dataset with 9.2G FLOPs. It notably enhances accuracy, recall rate, and average precision by 1.76%, 0.83%, and 2.27%, respectively.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"22 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CPP: a path planning method taking into account obstacle shadow hiding CPP：一种考虑障碍物阴影隐藏的路径规划方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01718-3

Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang

{"title":"CPP: a path planning method taking into account obstacle shadow hiding","authors":"Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang","doi":"10.1007/s40747-024-01718-3","DOIUrl":"https://doi.org/10.1007/s40747-024-01718-3","url":null,"abstract":"Path planning algorithms are crucial for the autonomous navigation and task execution of unmanned vehicles in battlefield environments. However, existing path planning algorithms often overlook the concealment effects of obstacles, which can lead to significant safety risks for unmanned vehicles during operation. To address this issue, we proposed a novel path planning method—Covert Path Planning (CPP)—that incorporated considerations for the shadow occlusion caused by obstacles. By accounting for these concealment effects, CPP aimed to enhance the safety and effectiveness of unmanned vehicles in complex and dynamic battlefield scenarios. It started by designing shadow areas in the configuration environment based on solar azimuth and altitude angles. A gravitational field model was then created using these shadow areas and the target point’s position to guide the path point movement, achieving a path with a higher safety coefficient. The method also dynamically adjusted step length according to gravitational forces to boost planning efficiency. Additionally, a deformed ellipse-based obstacle avoidance technique was introduced to enhance the vehicle’s ability to navigate around obstacles. We simplified the path by considering the relationship between path points and shadows. We also proposed a Minimum-Jerk Trajectory Optimization method with controllable path noise points, which enhanced path smoothness and reduced predictability. Comparative analysis showed that CPP significantly outperformed five other algorithms—RRT, Improved B-RRT, RRT*, Informed RRT*, and Potential Field-by reducing running time by 46.01% to 93.3%, increasing path safety by 10.42% to 83.44%, and improving path smoothness, making it particularly effective for path planning in tactical scenarios involving unmanned vehicles.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"161 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0